Super-Resolution is the process of constructing a high resolution image when a set of one or more low resolution input images is given . Traditionally , there are two methods exploited widely for enhancing the image via Super-Resolution viz . References [ 1 ] C. Dong , C. C. Loy , K. He , and X. Tang , “ Image Super-Resolution Using Deep Convolutional Networks ” , in IEEE Transactions on Pattern Analysis and Machine Intelligence 38 ( 2 ) , ( 2014 ) [ 2 ] Y. Tai , J. Yang , and X. Liu , “ Image Super-Resolution via Deep Recursive Residual Network ” , in IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) , ( 2017 ) [ 3 ] J. Johnson , A. Alahi , and L. Fei-Fei , “ Perceptual Losses for Real-Time Style Transfer and Super-Resolution ” , in 14th European Conference on Computer Vision ( ECCV ) , ( 2016 ) [ 4 ] C. Ledig , L. Theis , F. Huszár , J. Caballero , A. Cunnignham , A. Acosta , A. Aitken , A. Tejani , J. Totz , Z. Wang , and W. Shi , “ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network ” , in IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) , ( 2017 ) [ 5 ] B. Lim , S. Son , H. Kim , S. Nah , and K. Mu Lee , “ Enhanced Deep Residual Networks for Single Image Super-Resolution ” , in IEEE Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW ) , ( 2017 ) [ 6 ] M.S.M. Sajjadi , B. Schölkopf , and M. Hirsch , „ EnhanceNet : Single Image Super-Resolution through Automated Texture Synthesis ” in International Conference on Computer Vision , ( 2017 ) [ 7 ] M. Bevilacqua , A. Roumy , C. Guillemot , and M.L. Alberi-Morel , “ Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding ” , in British Machine Vision Conference ( BMVC ) , ( 2012 ) [ 8 ] J. Diebel , and S. Thrun , “ An Application of Markov Random Fields to Range Sensing ” , in Proceedings of Conference on Neural Information Processing Systems ( NIPS ) , ( 2005 ) [ 9 ] J. Park , H. Kim , Y.W. Tai , M.S. Brown , and I. Kweon , “ High Quality Depth Map Upsampling for 3D-TOF Cameras ” , in International Conference on Computer Vision ( ICCV ) , ( 2011 ) [ 10 ] D. Ferstl , C. Reinbacher , R. Ranftl , M. Rüther , and H. Bischof , “ Image Guided Depth Upsampling using Anisotropic Total Generalized Variation ” , in IEEE International Conference on Computer Vision ( ICCV ) , ( 2013 ) [ 11 ] C. Jung , S. Yu , and J. Kim , “ Intensity-guided edge-preserving depth upsampling through weighted L0 gradient minimization ” , in J. Vis . Image Super-Resolution is a complex method capable of converting a low-resolution image to a high-resolution image . Regarding many challenges of the Super-Resolution problem and a wide variety of its applications in image processing and man ’s interpretations , it is very crucial to find an operational method . In recent years , deep convolutional neural networks ( CNNs ) have achieved great success in Single Image Super-Resolution ( SISR ) . Most existing networks for Super-Resolution ( SR ) concentrate on wider or deeper network designs , leading to neglect of the feature correlations of intermediate layers . This paper presents a low-complexity saliency detector targeted towards efficient selective Super-Resolution ( SR ) . As a result , an improved efficient ATtentive-SELective Perceptual ( AT-SELP ) framework is presented . Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary . It was successfully applied on a wide range of applications like the textual image Super-Resolution . Nevertheless , its complexity limits enormously its application . Deep learning methods , in particular trained Convolutional Neural Networks ( CNNs ) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution ( SR ) . Invariably , a CNN is learned to map the low resolution ( LR ) image to its corresponding high resolution ( HR ) version in the spatial domain . In this paper , a novel method of blind Super-Resolution ( SR ) image restoration is presented . First , a learning based blur identification method is proposed to identify the blur parameter in which Sobel operator and Vector Quantization ( VQ ) are used for extracting feature vectors . Even small errors can yield a significant degradation of image quality that complicates any follow-on task such as object detection or classification . We focus on the problem of automatic quality assessment of Super-Resolution image reconstruction . We propose a bootstrap-based method that provides an objective metric quantifying reconstruction quality and thus allowing to readjust the reconstruction . Image and Video Super-Resolution problems become relevant in applied Deep learning due to recent results on using Convolutional Neural Networks and Adversarial Training method to solve such problems . This body of work has focused on conceiving or improving super-resolution methods , and validating them . However , existing SR approaches neglect to use attention mechanisms to transfer high-resolution ( HR ) textures from Ref images , which limits these approaches in challenging cases . In this paper , we propose a novel Texture Transformer Network for Image Super-Resolution ( TTSR ) , in which the LR and Ref images are formulated as queries and keys in a transformer , respectively . TTSR consists of four closely-related modules optimized for image generation tasks , including a learnable texture extractor by DNN , a relevance embedding module , a hard-attention module for texture transfer , and a soft-attention module for texture synthesis . In this paper , we study the problem of robust image fusion in the context of multi-frame super-resolution . Given multiple aligned noisy low-resolution images , image fusion produces a new image on a high-resolution grid . In order to utilize multiple low-resolution observations , both accurate image registration and sparse coding are required . Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration , followed by sparse coding to enhance the image resolution . In this paper , these two problems are solved by optimizing a single objective function . This paper addressed the problem of multi-frame image super resolution and implementation to fingerprint image . The use of computers in fingerprints recognition is highly desirable in many applications where security is an important concern . Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene , acquired under different conditions , and processes them together to synthesize one or more high-quality super-resolution images , with higher spatial frequency , and less noise and image blur than any of the original images . The inputs can take the form of medical images , surveillance footage , digital video , satellite terrain imagery , or images from many other sources . In this paper , a new method for multi-frame super-resolution reconstruction is proposed . It builds up with three steps : motion estimation , frame interpolation and fusion , deblurring . This paper provides subjective and objective a comparison of a multi-frame superresolution algorithm with four different single-frame algorithms . The multi-frame super-resolution reconstruction is based on an adaptive filtering approach , using least mean squares ( LMS ) . The single-frame methods are , respectively , bicubic interpolation , wavelet-based interpolation , edge-adaptive interpolation and interpolation using wide sense Markov random fields . Multi-frame super-resolution restoration refers to techniques for still-image and video restoration which utilize multiple observed images of an underlying scene to achieve the restoration of super-resolved imagery . An observation model which relates the measured data to the unknowns to be estimated is formulated to account for the registration of the multiple observations to a fixed reference frame as well as for spatial and temporal degradations resulting from characteristics of the optical system , sensor system and scene motion . Reviewing earlier results , we show how these conditions relate to the technique of image warping from the computer graphics literature and how these ideas may be applied to multi-frame restoration . We illustrate the application of these methods to multi-frame super-resolution restoration using a Bayesian inference framework to solve the ill-posed restoration inverse problem . The spatial resolution of digital images is the critical factor that affects photogrammetry precision . Single-frame , superresolution , image reconstruction is a typical underdetermined , inverse problem . To solve this type of problem , a compressive , sensing , pseudodictionary-based , superresolution reconstruction method is proposed in this study . The performance of block-matching sub-pixel motion estimation algorithms under the adverse conditions of image undersampling and additive noise is studied empirically . This study is motivated by the requirement for reliable sub-pixel accuracy motion estimates for motion compensated observation models used in multi-frame super-resolution image reconstruction . Idealized test functions which include translational scene motion are defined . A possible way of handling low resolution images is by using a multi-frame super-resolution algorithm . Multi-frame super-resolution image reconstruction aims at obtaining a high-resolution image by fusing a set of low-resolution images . Low-resolution images are usually subject to some degradation which causes substantial information loss . Multi frame super resolution ( SR ) reconstruction algorithms make use of complimentary information among low resolution ( LR ) images to yield a high resolution ( HR ) image . In this paper , we first present a fast partial differential equation ( PDE ) model for multi-frame image super resolution reconstruction . We then combine our proposed super resolution model with the local histogram equalization ( LHE ) , which perform super resolution and enhance image contrast simultaneously . Multi-frame super-resolution of images containing complex motion fields remains an elusive target requiring precise estimation of such motion between the frames . Although accuracy of optical flow algorithms has been increasing steadily it is not yet sufficient to provide confident subpixel resolution enhancement during super-resolution reconstruction . Multi-frame superresolution technology can get a high resolution image from multi-frame LR images . The last period processing of the bionic compound eye system can be considered as a multi-frame super-resolution processing . An iterative method is proposed built on the " shift and add " algorithm . The model parameters are learned using supervised or self - supervised training . We test this model on three tasks : non - uniform motion blur removal , lossy - compression artifact reduction and single image super resolution . We demonstrate that our model substantially outperforms state - of - the - art methods on all these tasks and is significantly faster than optimization - based approaches to deconvolution . document : Deep Laplacian Pyramid Networks for Fast and Accurate Super - Resolution Convolutional neural networks have recently demonstrated high - quality reconstruction for single - image super - resolution . In this paper , we propose the Laplacian Pyramid Super - Resolution Network ( LapSRN ) to progressively reconstruct the sub - band residuals of high - resolution images . document : Learning a Single Convolutional Super - Resolution Network for Multiple Degradations Recent years have witnessed the unprecedented success of deep convolutional neural networks ( CNNs ) in single image super - resolution ( SISR ) . However , existing CNN - based SISR methods mostly assume that a low - resolution ( LR ) image is bicubicly downsampled from a high - resolution ( HR ) image , thus inevitably giving rise to poor performance when the true degradation does not follow this assumption . document : Real - Time Video Super - Resolution with Spatio - Temporal Networks and Motion Compensation Convolutional neural networks have enabled accurate image super - resolution in real - time . However , recent attempts to benefit from temporal correlations in video super - resolution have been limited to naive or inefficient architectures . Submeter very-high-resolution ( VHR ) spaceborne imagery has potential in modeling the population dynamics of large ( > 0.6 m ) wild animals at large spatial and temporal scales , but has difficulty discerning small ( < 0.6 m ) animals at the species level , although high-resolution commercial satellites , such as WorldView-3 and -4 , have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode . This situation will not change unless the satellite image resolution is greatly improved in the future . Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas . Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting . In this paper we propose a data augmentation technique that can further boost the performance of satellite image super resolution tasks . A super-resolution convolutional neural network ( SRCNN ) was adopted as a state-of-the-art deep learning model to test the proposed data augmentation technique . The definition of satellite remote sensing image spatial resolution is given and its affecting factors are discussed . According to varied requirements for different research domains and mapping scales , the corresponding image spatial resolutions are proposed . For the surveying and mapping , the size of satellite remote sensing image spatial resolution for the series of scales of maps is uetermined by using remotely sensed imagery data . Based the analysis about spatial resolution of satellite remote sensing technology , the application is analyzed with cases about mainly remote sensing sources , and then some technical problems are pointed out . So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image . The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion . Therefore , this paper proposes a new method to test the horizontal accuracy of orthophoto image . A new aperture synthesis technology called RADSAR is an improved approach to satellite high resolution imaging . Two thin stick antennas are used to generate multiple parallel fan beams in the direction othogonal to the orbital path . To evaluate the super resolution accuracy with a real-life application , landcover classiﬁcation was adopted to assess the accuracy between super resolved Landsat-8 data and crowd-source data collected using the Google Earth interface . The accuracy achieved opens a wide ﬁeld of research with deep learning and data augmentation in the satellite image super resolution domain . Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample . For satellite remote sensing image , however , sample datasets covering large scope are still limited . Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area . This paper studies satellite remote sensing image super resolution that employs image processing techniques to reconstruct the high-resolution image from a set of low-resolution observations of the same scene . A heuristic approach for maximum a posteriori ( MAP ) estimate of desired high-resolution image based on markov random fields ( MRF ) is presented . A hybrid Bayes estimation(HBE ) algorithm for satellite superresolution reconstruction is proposed . Under the Bayesian framework , the optimal value of the ideal high resolution satellite image and the point spread function(PSF ) are estimated , and the Huber Markov random field(HMRF ) probability density distribution model for the ideal high resolution image by maximum likelihood(ML ) estimation are set up . The recovery of a HR image or video from its LR counter part is topic of great interest in digital image processing . This task , referred to as SR , finds direct applications in many areas such as HDTV , medical imaging , satellite imaging , face recognition and surveillance . The global SR problem assumes LR data to be a low - pass filtered ( blurred ) , downsampled and noisy version of HR data . Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved . My interest is in the 3D reconstruction of 3D objects from intensity images . The talk will cover two topics . On synthetic benchmarks based on ShapeNet ( Chang et al. Shapenet : an information-rich 3d model repository , 2015 . arXiv:1512.03012 ) and ModelNet ( Wu et al. , in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2015 ) as well as on real robotics data from KITTI ( Geiger et al. , in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2012 ) and Kinect ( Yang et al. , 3d object dense reconstruction from a single depth view , 2018 . arXiv:1802.00411 ) , we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of Dai et al. ( in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2017 ) and outperforms the data-driven approach of Engelmann et al. ( in : Proceedings of the German conference on pattern recognition ( GCPR ) , 2016 ) , while requiring less supervision and being significantly faster . Based on the detailed analysis of MC ( Marching Cubes ) algorithm which is widely used for extracting iso -surface from volume data field , an improved algorithm is proposed . The 3D object image reconstruction is realized by utilizing the Tri-point linear interpolation to replace the polygon vertex and then performing a smooth process in this normal vector filed . The model can be used to train a 3D generator network via MCMC teaching . The conditional version of the 3D shape descriptor net can be used for 3D object recovery and 3D object super-resolution . Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D shape analysis . It is based on the concept of volume intersection , but it is substantially different from the existing octree-based reconstruction methods in the aspects of data structure , reconstruction process and representation uniqueness under a 2D rigid motion . For visualizing the 3D reconstructed object geometry a conversion from the line-based geometric model to a bounded triangular mesh model is developed . The experimental results show that the method is capable of capturing the different details of the object . The super-resolution reconstruction is an important method for improving the image quality , and the objective world can be better understood from 3D super-resolution model than from 2D images . In this paper , the image super-resolution reconstruction algorithm based on wavelet and the 3D geometric modeling technology were combined to study the 3D super-resolution reconstruction of images . Firstly , the different super-resolution images of different angles were obtained by using the wavelet super-resolution reconstruction . We have developed a 3D imaging modality , multifocal plane microscopy ( MUM ) , that provides a powerful approach for 3D single molecule tracking and 3D superresolution microscopy . Here we review the technical challenges associated with 3D single molecule localization that is fundamental to both 3D tracking and 3D superresolution , and discuss how MUM overcomes these problems . Unfortunately , such narrow bandwidth methods to not allow sufficient resolution to reconstruct objects of interest in 3 dimensions at low frequencies and with small apertures . We propose a method that is computationally efficient and allows dynamic use of spectrum to achieve high resolution 3 dimensional reconstruction of objects from small or distributed apertures . This method also allows available spectrum bands to be used on a non-interference basis . Several optical approaches exist such as passive stereo , which have problems with textureless objects , and LIDARs which are slow and provide low lateral resolution . In this work we explore the use of structured light , to achieve high-resolution 3D of objects in an underwater environment . We have adapted a Gray Code Phase Stepping ( GCPS ) structured light approach to be more robust to the contrast degradation caused by the scattering and attenuation of light in water .
Super-Resolution is the process of constructing a high resolution image when a set of one or more low resolution input images is given . Traditionally , there are two methods exploited widely for enhancing the image via Super-Resolution viz .
References [ 1 ] C. Dong , C. C. Loy , K. He , and X. Tang , “ Image Super-Resolution Using Deep Convolutional Networks ” , in IEEE Transactions on Pattern Analysis and Machine Intelligence 38 ( 2 ) , ( 2014 ) [ 2 ] Y. Tai , J. Yang , and X. Liu , “ Image Super-Resolution via Deep Recursive Residual Network ” , in IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) , ( 2017 ) [ 3 ] J. Johnson , A. Alahi , and L. Fei-Fei , “ Perceptual Losses for Real-Time Style Transfer and Super-Resolution ” , in 14th European Conference on Computer Vision ( ECCV ) , ( 2016 ) [ 4 ] C. Ledig , L. Theis , F. Huszár , J. Caballero , A. Cunnignham , A. Acosta , A. Aitken , A. Tejani , J. Totz , Z. Wang , and W. Shi , “ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network ” , in IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) , ( 2017 ) [ 5 ] B. Lim , S. Son , H. Kim , S. Nah , and K. Mu Lee , “ Enhanced Deep Residual Networks for Single Image Super-Resolution ” , in IEEE Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW ) , ( 2017 ) [ 6 ] M.S.M. Sajjadi , B. Schölkopf , and M. Hirsch , „ EnhanceNet : Single Image Super-Resolution through Automated Texture Synthesis ” in International Conference on Computer Vision , ( 2017 ) [ 7 ] M. Bevilacqua , A. Roumy , C. Guillemot , and M.L. Alberi-Morel , “ Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding ” , in British Machine Vision Conference ( BMVC ) , ( 2012 ) [ 8 ] J. Diebel , and S. Thrun , “ An Application of Markov Random Fields to Range Sensing ” , in Proceedings of Conference on Neural Information Processing Systems ( NIPS ) , ( 2005 ) [ 9 ] J. Park , H. Kim , Y.W. Tai , M.S. Brown , and I. Kweon , “ High Quality Depth Map Upsampling for 3D-TOF Cameras ” , in International Conference on Computer Vision ( ICCV ) , ( 2011 ) [ 10 ] D. Ferstl , C. Reinbacher , R. Ranftl , M. Rüther , and H. Bischof , “ Image Guided Depth Upsampling using Anisotropic Total Generalized Variation ” , in IEEE International Conference on Computer Vision ( ICCV ) , ( 2013 ) [ 11 ] C. Jung , S. Yu , and J. Kim , “ Intensity-guided edge-preserving depth upsampling through weighted L0 gradient minimization ” , in J. Vis .
Image Super-Resolution is a complex method capable of converting a low-resolution image to a high-resolution image . Regarding many challenges of the Super-Resolution problem and a wide variety of its applications in image processing and man ’s interpretations , it is very crucial to find an operational method .
In recent years , deep convolutional neural networks ( CNNs ) have achieved great success in Single Image Super-Resolution ( SISR ) . Most existing networks for Super-Resolution ( SR ) concentrate on wider or deeper network designs , leading to neglect of the feature correlations of intermediate layers .
This paper presents a low-complexity saliency detector targeted towards efficient selective Super-Resolution ( SR ) . As a result , an improved efficient ATtentive-SELective Perceptual ( AT-SELP ) framework is presented .
Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary . It was successfully applied on a wide range of applications like the textual image Super-Resolution . Nevertheless , its complexity limits enormously its application .
Deep learning methods , in particular trained Convolutional Neural Networks ( CNNs ) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution ( SR ) . Invariably , a CNN is learned to map the low resolution ( LR ) image to its corresponding high resolution ( HR ) version in the spatial domain .
In this paper , a novel method of blind Super-Resolution ( SR ) image restoration is presented . First , a learning based blur identification method is proposed to identify the blur parameter in which Sobel operator and Vector Quantization ( VQ ) are used for extracting feature vectors .
Even small errors can yield a significant degradation of image quality that complicates any follow-on task such as object detection or classification . We focus on the problem of automatic quality assessment of Super-Resolution image reconstruction . We propose a bootstrap-based method that provides an objective metric quantifying reconstruction quality and thus allowing to readjust the reconstruction .
Image and Video Super-Resolution problems become relevant in applied Deep learning due to recent results on using Convolutional Neural Networks and Adversarial Training method to solve such problems . This body of work has focused on conceiving or improving super-resolution methods , and validating them .
However , existing SR approaches neglect to use attention mechanisms to transfer high-resolution ( HR ) textures from Ref images , which limits these approaches in challenging cases . In this paper , we propose a novel Texture Transformer Network for Image Super-Resolution ( TTSR ) , in which the LR and Ref images are formulated as queries and keys in a transformer , respectively . TTSR consists of four closely-related modules optimized for image generation tasks , including a learnable texture extractor by DNN , a relevance embedding module , a hard-attention module for texture transfer , and a soft-attention module for texture synthesis .
In this paper , we study the problem of robust image fusion in the context of multi-frame super-resolution . Given multiple aligned noisy low-resolution images , image fusion produces a new image on a high-resolution grid .
In order to utilize multiple low-resolution observations , both accurate image registration and sparse coding are required . Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration , followed by sparse coding to enhance the image resolution . In this paper , these two problems are solved by optimizing a single objective function .
This paper addressed the problem of multi-frame image super resolution and implementation to fingerprint image . The use of computers in fingerprints recognition is highly desirable in many applications where security is an important concern .
Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene , acquired under different conditions , and processes them together to synthesize one or more high-quality super-resolution images , with higher spatial frequency , and less noise and image blur than any of the original images . The inputs can take the form of medical images , surveillance footage , digital video , satellite terrain imagery , or images from many other sources .
In this paper , a new method for multi-frame super-resolution reconstruction is proposed . It builds up with three steps : motion estimation , frame interpolation and fusion , deblurring .
This paper provides subjective and objective a comparison of a multi-frame superresolution algorithm with four different single-frame algorithms . The multi-frame super-resolution reconstruction is based on an adaptive filtering approach , using least mean squares ( LMS ) . The single-frame methods are , respectively , bicubic interpolation , wavelet-based interpolation , edge-adaptive interpolation and interpolation using wide sense Markov random fields .
Multi-frame super-resolution restoration refers to techniques for still-image and video restoration which utilize multiple observed images of an underlying scene to achieve the restoration of super-resolved imagery . An observation model which relates the measured data to the unknowns to be estimated is formulated to account for the registration of the multiple observations to a fixed reference frame as well as for spatial and temporal degradations resulting from characteristics of the optical system , sensor system and scene motion .
Reviewing earlier results , we show how these conditions relate to the technique of image warping from the computer graphics literature and how these ideas may be applied to multi-frame restoration . We illustrate the application of these methods to multi-frame super-resolution restoration using a Bayesian inference framework to solve the ill-posed restoration inverse problem .
The spatial resolution of digital images is the critical factor that affects photogrammetry precision . Single-frame , superresolution , image reconstruction is a typical underdetermined , inverse problem . To solve this type of problem , a compressive , sensing , pseudodictionary-based , superresolution reconstruction method is proposed in this study .
The performance of block-matching sub-pixel motion estimation algorithms under the adverse conditions of image undersampling and additive noise is studied empirically . This study is motivated by the requirement for reliable sub-pixel accuracy motion estimates for motion compensated observation models used in multi-frame super-resolution image reconstruction . Idealized test functions which include translational scene motion are defined .
A possible way of handling low resolution images is by using a multi-frame super-resolution algorithm . Multi-frame super-resolution image reconstruction aims at obtaining a high-resolution image by fusing a set of low-resolution images . Low-resolution images are usually subject to some degradation which causes substantial information loss .
Multi frame super resolution ( SR ) reconstruction algorithms make use of complimentary information among low resolution ( LR ) images to yield a high resolution ( HR ) image . In this paper , we first present a fast partial differential equation ( PDE ) model for multi-frame image super resolution reconstruction . We then combine our proposed super resolution model with the local histogram equalization ( LHE ) , which perform super resolution and enhance image contrast simultaneously .
Multi-frame super-resolution of images containing complex motion fields remains an elusive target requiring precise estimation of such motion between the frames . Although accuracy of optical flow algorithms has been increasing steadily it is not yet sufficient to provide confident subpixel resolution enhancement during super-resolution reconstruction .
Multi-frame superresolution technology can get a high resolution image from multi-frame LR images . The last period processing of the bionic compound eye system can be considered as a multi-frame super-resolution processing . An iterative method is proposed built on the " shift and add " algorithm .
The model parameters are learned using supervised or self - supervised training . We test this model on three tasks : non - uniform motion blur removal , lossy - compression artifact reduction and single image super resolution . We demonstrate that our model substantially outperforms state - of - the - art methods on all these tasks and is significantly faster than optimization - based approaches to deconvolution .
document : Deep Laplacian Pyramid Networks for Fast and Accurate Super - Resolution Convolutional neural networks have recently demonstrated high - quality reconstruction for single - image super - resolution . In this paper , we propose the Laplacian Pyramid Super - Resolution Network ( LapSRN ) to progressively reconstruct the sub - band residuals of high - resolution images .
document : Learning a Single Convolutional Super - Resolution Network for Multiple Degradations Recent years have witnessed the unprecedented success of deep convolutional neural networks ( CNNs ) in single image super - resolution ( SISR ) . However , existing CNN - based SISR methods mostly assume that a low - resolution ( LR ) image is bicubicly downsampled from a high - resolution ( HR ) image , thus inevitably giving rise to poor performance when the true degradation does not follow this assumption .
document : Real - Time Video Super - Resolution with Spatio - Temporal Networks and Motion Compensation Convolutional neural networks have enabled accurate image super - resolution in real - time . However , recent attempts to benefit from temporal correlations in video super - resolution have been limited to naive or inefficient architectures .
Submeter very-high-resolution ( VHR ) spaceborne imagery has potential in modeling the population dynamics of large ( > 0.6 m ) wild animals at large spatial and temporal scales , but has difficulty discerning small ( < 0.6 m ) animals at the species level , although high-resolution commercial satellites , such as WorldView-3 and -4 , have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode . This situation will not change unless the satellite image resolution is greatly improved in the future . Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas .
Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting . In this paper we propose a data augmentation technique that can further boost the performance of satellite image super resolution tasks . A super-resolution convolutional neural network ( SRCNN ) was adopted as a state-of-the-art deep learning model to test the proposed data augmentation technique .
The definition of satellite remote sensing image spatial resolution is given and its affecting factors are discussed . According to varied requirements for different research domains and mapping scales , the corresponding image spatial resolutions are proposed . For the surveying and mapping , the size of satellite remote sensing image spatial resolution for the series of scales of maps is uetermined by using remotely sensed imagery data . Based the analysis about spatial resolution of satellite remote sensing technology , the application is analyzed with cases about mainly remote sensing sources , and then some technical problems are pointed out .
So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image . The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion . Therefore , this paper proposes a new method to test the horizontal accuracy of orthophoto image .
A new aperture synthesis technology called RADSAR is an improved approach to satellite high resolution imaging . Two thin stick antennas are used to generate multiple parallel fan beams in the direction othogonal to the orbital path .
To evaluate the super resolution accuracy with a real-life application , landcover classiﬁcation was adopted to assess the accuracy between super resolved Landsat-8 data and crowd-source data collected using the Google Earth interface . The accuracy achieved opens a wide ﬁeld of research with deep learning and data augmentation in the satellite image super resolution domain .
Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample . For satellite remote sensing image , however , sample datasets covering large scope are still limited . Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area .
This paper studies satellite remote sensing image super resolution that employs image processing techniques to reconstruct the high-resolution image from a set of low-resolution observations of the same scene . A heuristic approach for maximum a posteriori ( MAP ) estimate of desired high-resolution image based on markov random fields ( MRF ) is presented .
A hybrid Bayes estimation(HBE ) algorithm for satellite superresolution reconstruction is proposed . Under the Bayesian framework , the optimal value of the ideal high resolution satellite image and the point spread function(PSF ) are estimated , and the Huber Markov random field(HMRF ) probability density distribution model for the ideal high resolution image by maximum likelihood(ML ) estimation are set up .
The recovery of a HR image or video from its LR counter part is topic of great interest in digital image processing . This task , referred to as SR , finds direct applications in many areas such as HDTV , medical imaging , satellite imaging , face recognition and surveillance . The global SR problem assumes LR data to be a low - pass filtered ( blurred ) , downsampled and noisy version of HR data .
Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved .
My interest is in the 3D reconstruction of 3D objects from intensity images . The talk will cover two topics .
On synthetic benchmarks based on ShapeNet ( Chang et al. Shapenet : an information-rich 3d model repository , 2015 . arXiv:1512.03012 ) and ModelNet ( Wu et al. , in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2015 ) as well as on real robotics data from KITTI ( Geiger et al. , in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2012 ) and Kinect ( Yang et al. , 3d object dense reconstruction from a single depth view , 2018 . arXiv:1802.00411 ) , we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of Dai et al. ( in : Proceedings of IEEE conference on computer vision and pattern recognition ( CVPR ) , 2017 ) and outperforms the data-driven approach of Engelmann et al. ( in : Proceedings of the German conference on pattern recognition ( GCPR ) , 2016 ) , while requiring less supervision and being significantly faster .
Based on the detailed analysis of MC ( Marching Cubes ) algorithm which is widely used for extracting iso -surface from volume data field , an improved algorithm is proposed . The 3D object image reconstruction is realized by utilizing the Tri-point linear interpolation to replace the polygon vertex and then performing a smooth process in this normal vector filed .
The model can be used to train a 3D generator network via MCMC teaching . The conditional version of the 3D shape descriptor net can be used for 3D object recovery and 3D object super-resolution . Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D shape analysis .
It is based on the concept of volume intersection , but it is substantially different from the existing octree-based reconstruction methods in the aspects of data structure , reconstruction process and representation uniqueness under a 2D rigid motion . For visualizing the 3D reconstructed object geometry a conversion from the line-based geometric model to a bounded triangular mesh model is developed . The experimental results show that the method is capable of capturing the different details of the object .
The super-resolution reconstruction is an important method for improving the image quality , and the objective world can be better understood from 3D super-resolution model than from 2D images . In this paper , the image super-resolution reconstruction algorithm based on wavelet and the 3D geometric modeling technology were combined to study the 3D super-resolution reconstruction of images . Firstly , the different super-resolution images of different angles were obtained by using the wavelet super-resolution reconstruction .
We have developed a 3D imaging modality , multifocal plane microscopy ( MUM ) , that provides a powerful approach for 3D single molecule tracking and 3D superresolution microscopy . Here we review the technical challenges associated with 3D single molecule localization that is fundamental to both 3D tracking and 3D superresolution , and discuss how MUM overcomes these problems .
Unfortunately , such narrow bandwidth methods to not allow sufficient resolution to reconstruct objects of interest in 3 dimensions at low frequencies and with small apertures . We propose a method that is computationally efficient and allows dynamic use of spectrum to achieve high resolution 3 dimensional reconstruction of objects from small or distributed apertures . This method also allows available spectrum bands to be used on a non-interference basis .
Several optical approaches exist such as passive stereo , which have problems with textureless objects , and LIDARs which are slow and provide low lateral resolution . In this work we explore the use of structured light , to achieve high-resolution 3D of objects in an underwater environment . We have adapted a Gray Code Phase Stepping ( GCPS ) structured light approach to be more robust to the contrast degradation caused by the scattering and attenuation of light in water .
A robust two-stage super-resolution algorithm Optimized deep learning algorithms for application with data from PMDcameras Super-resolution using lightweight detailnet network Multilevel and Multiscale Network for Single-Image Super-Resolution Efficient Super-Resolution driven by saliency selectivity Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution Orthogonally Regularized Deep Networks for Image Super-Resolution A VQ-Based Blind Super-Resolution Algorithm How good is your Super-Resolution Image? Quality assurance in image reconstruction using the bootstrap A GAN to Fight Video-related Traffic Flooding: Super-resolution Learning Texture Transformer Network for Image Super-Resolution Total least square kernel regression Double sparsity for multi-frame super resolution Fingerprint image enhancement by super resolution with early stopping Machine learning in multi-frame image super-resolution A New Method for Multi-frame Super-Resolution Reconstruction Assessment of single-frame resolution enhancement algorithms Linear models for multiframe super-resolution restoration under nonaffine registration and spatially varying PSF Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction Block-matching subpixel motion estimation from noisy undersampled frames: an empirical performance evaluation Improving Face Recognition in Low Quality Video Sequences: Single Frame vs Multi-frame Super-Resolution A PDE approach to super-resolution with contrast enhancement Confidence measures of optical flow estimation suitable for multiframe superresolution Reconstruction of Bionic Compound Eye Images Based on Superresolution Algorithm Image Reconstruction with Predictive Filter Flow Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review Data Augmentation Approaches for Satellite Image Super-Resolution Feasibility of Satellite Remote Sensing Image About Spatial Resolution RESEARCH ON HORIZONTAL ACCURACY METHOD OF HIGH SPATIAL RESOLUTION REMOTELY SENSED ORTHOPHOTO IMAGE Synthetic Array Radiometry Spatial resolution enhancement using deep learning and data augmentation for cloud-based infrastructure A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY Satellite remote sensing image super resolution based on markov random fields HBE Satellite Image Super-resolution Reconstruction with HRMF Prior Model Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Scale Model Refinement for 3D Reconstruction Learning 3D Shape Completion Under Weak Supervision An improved algorithm for medical image reconstruction based on optimizing of the iso-surface Learning Descriptor Networks for 3D Shape Synthesis and Analysis 3D shape recovery of complex objects from multiple silhouette images The Study of 3D super-resolution geometric modeling based on wavelet analysis 3D single molecule tracking and superresolution microscopy using multifocal plane microscopy 3-dimensional object reconstruction from frequency diverse RF systems High-resolution structured light D sensor for autonomous underwater inspection
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ICIP ICPR ICASSP ICIC (1) ICASSP LATINCOM CSO (1) Computational Imaging ICIAP (1) Visual Information Processing ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IGARSS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISBI AIPR
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