Image super-resolution is of great interest in medical imaging field . However , different from natural images studied in computer vision field , the low-resolution ( LR ) medical imaging data is often a stack of high-resolution ( HR ) 2D slices with large slice thickness . In iterative back projection ( IBP ) algorithm , the initial value is estimated through wavelet locally adaptive interpolation method , other than in the traditional interpolation way . Image super resolution reconstruction is performed on multiple simulated low resolution images , experimental results show that the initial value plays an important part in super resolution reconstruction algorithm . The reconstructed image achieves better visual effect and higher peak signal to noise ratio ( PSNR ) . The experimental results show that this method is easier to implement and accords with the Human Visual System ( HVS ) . Combine with other methods , this method can be used as a reference method in quality assessment of image super resolution reconstruction . Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients , whose distribution densities can be approximated by the Gaussian mixture . Because wavelet-domain HMT accurately characterizes the statistics of real-world images , we reasonably specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem . And the cycle-spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images . A MAP method based on adaptive regularization was proposed for image super-resolution restoration , the prior distribution of the image was assumed in Gaussian-Gibbs field . The solution was adaptively regularized through parameters estimating of prior distribution . To address this challenge , this paper proposes a wavelet-domain generative adversarial method that can ultra-resolve a very low-resolution ( like $ $ 16\times 16$$16 × 16 or even $ $ 8\times 8$$8 × 8 ) face image to its larger version of multiple upscaling factors ( $ $ 2\times $ $ 2 × to $ $ 16\times $ $ 16 × ) in a unified framework . Different from the most existing studies that hallucinate faces in image pixel domain , our method firstly learns to predict the wavelet information of HR face images from its corresponding LR inputs before image-level super-resolution . To capture both global topology information and local texture details of human faces , a flexible and extensible generative adversarial network is designed with three types of losses : ( 1 ) wavelet reconstruction loss aims to push wavelets closer with the ground-truth ; ( 2 ) wavelet adversarial loss aims to generate realistic wavelets ; ( 3 ) identity preserving loss aims to help identity information recovery . The procedure allows the incorporation of the more general projective camera motion model into the framework , instead of only displacement and rotational models . Several simulations that compare the implementations of the algorithm presented here with other related approaches help illustrate the suitability of SGWs ( coupled with hard or soft thresholding ) in the task of image sequence superresolution with simultaneous noise filtering . © 2004 Explicit inter-frame pixel correspondences are not required for our super-resolution procedure , thus we can avoid a huge computation time and obtain improved depth map in the accuracy and resolution as well as high-resolution images with reasonable time . The superiority of our algorithm is demonstrated by presenting the improved depth map accuracy , image super-resolution results , and camera pose estimation . For existing methods , their denoising performance does not differ much , while our model achieves 0.38dB , 0.47dB , 0.49dB and 0.42dB higher of PSNR over WNNM . subsection : Image super - resolution The evaluation on Set5 is shown in Table [ reference ] . WuzhenShi , FengJiang , DebinZhao SchoolofComputerScienceandTechnology , HarbinInstituteofTechnology , Harbin150001 , China Image super - resolution , convolutional neural network , multi - scale information , dilated convolution , inception module section : Introduction section : Introduction In the problem of single - image super - resolution ( SR ) , the aim is to recover a high - resolution ( HR ) image from a single low - resolution ( LR ) image . In recent years , SR performance has been significantly improved due to rapid developments in deep neural networks ( DNNs ) . section : Introduction Single image super - resolution ( SISR ) aims to recover a high - resolution ( HR ) version of a low - resolution ( LR ) input . As a classical problem , SISR is still an active yet challenging research topic in the field of computer vision due to its ill - poseness nature and high practical values . section : Introduction Single image super - resolution ( SR ) aims at reconstructing a high - resolution ( HR ) image by restoring the high frequencies details from a single low - resolution ( LR ) image . SR is heavily ill - posed since multiple HR patches could correspond to the same LR image patch . Let be the input LR image and be the set of network parameters to be optimized . Our goal is to learn a mapping function for generating a high - resolution image that is close to the ground truth HR image . We denote the residual image at level by , the upscaled LR image by and the corresponding HR images by . subsection : Image Super - Resolution Super - resolution is the process of recovering a high resolution image from a low resolution image while generating realistic and plausible details . Following PixelRecursiveSuperResolution , in our experimental setup we enlarge an pixel image four - fold to , a process that is massively underspecified : the model has to generate aspects such as texture of hair , makeup , skin and sometimes even gender that can not possibly be recovered from the source image . paragraph : Downsampling Input Image resolution is the most critical factor that affects running speed as analyzed in Sec . [ reference ] . 3 ) We demonstrate that deep learning is useful in the classical computer vision problem of superresolution , and can achieve good quality and speed . A preliminary version of this work was presented earlier In particular , it can readily deal with three channels simultaneously by setting the input channels to c = 3 . In the following experiments , we explore different training strategies for color image super - resolution , and subsequently evaluate their performance on different channels . Implementation details . To make the best use of the previous estimations and shared redundancy across the consecutive video frames , here we propose a scene and class agnostic , fully convolutional neural network model for 4 × video super-resolution . One stage of our network is composed of a motion compensation based input subnetwork , a blending backbone , and a spatial upsampling subnetwork . A second obstacle is the non-differentiability and the non-convexity of some of the terms of the cost function : the non-differentiability stems from the use of regularization terms of the state of the art ( e.g. , to enforce sparsity ) whereas the non-convexity appears as soon as the motion describing the video is unknown . In this paper , we propose an overall algorithmic framework to address the video super-resolution problem . Our approach is based on fast gradient evaluation methods and modern optimization techniques for non-differentiable/non-convex problems . Multi-frame super resolution has been well studied in recent years , but blur kernel is always assumed to be known in video super resolution problem . Most blind deconvolution algorithm can both estimate the blur kernel and the sharp image . To stabilize the problem and make its solution more accurate , we develop two video super-resolution techniques : 1 ) a 2D autoregressive modeling and interpolation tech­ nique for video super-resolution reconstruction , with model parameters estimated from multiple registered low-resolution frames ; 2 ) the use of image model as a regu­ larization term to improve the performance of the traditional video super-resolution algorithm . We further investigate the interactions of various unknown variables involved in video super-resolution reconstruction , including motion parameters , high-resolution pixel intensities and the parameters of the image model used for regularization . We succeed in developing a joint estimation technique that infers these unknowns simul­ taneously to achieve statistical consistency among them . Regularization-based and a fast non-iterative methods using optical flow estimation are suggested for video data super-resolution with correction of non-uniform illumination . This paper introduces a novel large dataset for video deblurring , video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges . The video deblurring and video super-resolution challenges are each the first challenge of its kind , with 4 competitions , hundreds of participants and tens of proposed solutions . Our newly collected REalistic and Diverse Scenes dataset ( REDS ) was employed by the challenges . Video super-resolution enhancement technique has two main aims . The first is reinforcing the shortage of video device frame rate , describing the details of fast-moving object . Abstract . Video-frame registration is a critical step in the video super-resolution process . However , many existing registration methods can handle only small local neighborhood movements or overall affine transformations . Abstract In view of the common existing problems in present video-to-video super-resolution reconstruction , this paper proposes a pioneering video-to-video super-resolution reconstruction algorithm based on segmentation and space – time regularisation to solve these problems . First , a video-to-video super-resolution reconstruction algorithm based on segmentation is proposed to eliminate reconstructed temporal ringing and to improve the times of reconstruction . document : Real - Time Single Image and Video Super - Resolution Using an Efficient Sub - Pixel Convolutional Neural Network Recently , several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super - resolution . 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 . In this paper , we introduce spatio - temporal sub - pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real - time speed . This brings Amdhal 's law in effect with full force , so that message-passing latency and bandwidth severely restrict performance . The authors examine an application from this domain , stereo image processing , which has been implemented in Adapt , a niche language for parallel image processing implemented on the Carnegie Mellon-Intel Corporation iWarp . High performance has been achieved for this application . In this paper , stereo image reconstruction using regularized adaptive disparity estimation is proposed . That is , by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm , the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors . The MATLAB software can do reconstruction of any object if it has two images from two different positions . The stereo image reconstruction it 's a very demanded task , due to the fact that 3D imaging became more popular in the last years . The 3D object than it can be used as a normal 3D generated object , the only difference is that it 's much simpler to make , it needs only the images from different sides of the object and with the usage of some image processing software the 3D image can be generated . With the popularity of dual cameras in recently released smart phones , a growing number of super-resolution ( SR ) methods have been proposed to enhance the resolution of stereo image pairs . However , the lack of high-quality stereo datasets has limited the research in this area . In this paper , we present a method for stereo super-resolution which employs a deep network . The network is trained using the residual image so as to obtain a high resolution image from two , low resolution views . Stereo vision range sensors have been used for several decades to obtain 3D information of the scene . Still , the major issue involved in the stereo analysis of images is the correspondence problem . In this paper we consider the correspondence problem only for point clouds . A new multistage adaptive robust ( MAR ) algorithm combined with a multiresolution coarse to fine matching model is developed for robust stereo analysis . Stereo analysis of remotely sensed images is useful for a variety of applications including cloud height measurement and digital terrain models . Time sequential stereoscopic observations of clouds from meteorological satellites provide a basic analysis tool for a broad spectrum of applications including numerical weather prediction , cloud modeling , and global climate understanding . The system includes one to five videotape color CCD video cameras , one calibration color CCD video camera , two to eight projectors , one large screen display terminal , one calibration plate , and the compute set multi-path synchronization image acquisition card by which it is connected with the video camera , and multi-path output display card by which it is connected with the projector . The method includes the steps that image source video frequency coding , projector calibration , stereo image mosaicking . The invention can realize large screen vivid three-dimensional multi-projection display based on computer vision technology . This paper demonstrates a gesture recognition approach based on binocular camera . The binocular vision system can deal with stereo imaging problem using disparity map . After the cameras are calibrated , the approach uses skin color model and depth information to separate the hand from the environment in the image . The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages , sent between the distributed computers . We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery , and show that inference is possible with more than 200 labels in images larger than 10 MPixels . Advancements over the last decade in video acquisition and display technologies lead to a continuous increase of video content resolution . These aspects combined with the shift towards cloud multimedia services and the underway adoption of High Efficiency Video Coding standards ( HEVC ) create a lot of interest for Super-Resolution ( SR ) and video enhancing techniques . Recent works showed that proximal based convex optimization approaches provide a promising direction in video restoration . Kernel regression based super-resolution approaches are promising , but kernel selection is a critical problem . In order to avoid demanding and time-consuming cross validation for kernel selection , we propose multi-kernel regression ( MKR ) model for image Super-Resolution ( SR ) . Considering the multi-kernel regression model is prohibited when the training data is large-scale , we further propose a prototype MKR algorithm which can reduce the computational complexity . Face Super-Resolution ( SR ) is a subfield of the SR domain that specifically targets the reconstruction of face images . The main challenge of face SR is to restore essential facial features without distortion . In many applications , a high-resolution image is desired from available multiple degraded image frames , especially in surveillance recording . This paper represents a new objet based Super-Resolution using Phase Template Matching . This is a problem of extracting an object from a series of images which extracted image has more high frequency information than any one of the input images . The present work extends the scope of Optical Super-Resolution to imaging systems with spatially-varying blur , by using sinusoidal illumination . It also establishes that knowledge of the space-variant blur is not a pre-requisite for super-resolution . Image Super-Resolution : Historical Overview and Future Challenges , J. Yang and T. Huang Introduction to Super-Resolution Notations Techniques for Super-Resolution Challenge issues for Super-Resolution Super-Resolution Using Adaptive Wiener Filters , R.C. Hardie Introduction Observation Model AWF SR Algorithms Experimental Results Conclusions Acknowledgments Locally Adaptive Kernel Regression for Space-Time Super-Resolution , H. Takeda and P. Milanfar Introduction Adaptive Kernel Regression Examples Conclusion AppendiX Super-Resolution With Probabilistic Motion Estimation , M. Protter and M. Elad Introduction Classic Super-Resolution : Background The Proposed Algorithm Experimental Validation Summary Spatially Adaptive Filtering as Regularization in Inverse Imaging , A. Danielyan , A. Foi , V. Katkovnik , and K. Egiazarian Introduction Iterative filtering as regularization Compressed sensing Super-resolution Conclusions Registration for Super-Resolution , P. Vandewalle , L. Sbaiz , and However , they require thousands of gradient updates , i.e. , long inference time . In this paper , we present Meta-Transfer Learning for Zero-Shot Super-Resolution ( MZSR ) , which leverages ZSSR . Precisely , it is based on finding a generic initial parameter that is suitable for internal learning . Aiming at robust face recognition , this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly . Possible solutions to the problem are discussed and grouped into three categories : gray-scale resolution invariant features , gray-scale degradation modeling and Gray-scale Super-Resolution ( GSR ) . Then , we propose a Coupled Subspace Analysis ( CSA ) based GSR method to recover the high gray-scale resolution image from a single input LGR image . Single Image Super-Resolution task based on GANs has shown a great improvement in all methods , but still has the optimization problems of texture details and distortion of local regions in super-resolved images . In this paper , we proposed an attention-based GAN architecture to solve preceding problems . We propose a two-step neural approach for face Super-Resolution ( SR ) to improve face recognition performance . It consists in first performing generic SR on the entire image , based on Convolutional Neural Networks , followed by a specific local SR step for each facial component , using neural autoencoders .
Image super-resolution is of great interest in medical imaging field . However , different from natural images studied in computer vision field , the low-resolution ( LR ) medical imaging data is often a stack of high-resolution ( HR ) 2D slices with large slice thickness .
In iterative back projection ( IBP ) algorithm , the initial value is estimated through wavelet locally adaptive interpolation method , other than in the traditional interpolation way . Image super resolution reconstruction is performed on multiple simulated low resolution images , experimental results show that the initial value plays an important part in super resolution reconstruction algorithm . The reconstructed image achieves better visual effect and higher peak signal to noise ratio ( PSNR ) .
The experimental results show that this method is easier to implement and accords with the Human Visual System ( HVS ) . Combine with other methods , this method can be used as a reference method in quality assessment of image super resolution reconstruction .
Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients , whose distribution densities can be approximated by the Gaussian mixture . Because wavelet-domain HMT accurately characterizes the statistics of real-world images , we reasonably specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem . And the cycle-spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images .
A MAP method based on adaptive regularization was proposed for image super-resolution restoration , the prior distribution of the image was assumed in Gaussian-Gibbs field . The solution was adaptively regularized through parameters estimating of prior distribution .
To address this challenge , this paper proposes a wavelet-domain generative adversarial method that can ultra-resolve a very low-resolution ( like $ $ 16\times 16$$16 × 16 or even $ $ 8\times 8$$8 × 8 ) face image to its larger version of multiple upscaling factors ( $ $ 2\times $ $ 2 × to $ $ 16\times $ $ 16 × ) in a unified framework . Different from the most existing studies that hallucinate faces in image pixel domain , our method firstly learns to predict the wavelet information of HR face images from its corresponding LR inputs before image-level super-resolution . To capture both global topology information and local texture details of human faces , a flexible and extensible generative adversarial network is designed with three types of losses : ( 1 ) wavelet reconstruction loss aims to push wavelets closer with the ground-truth ; ( 2 ) wavelet adversarial loss aims to generate realistic wavelets ; ( 3 ) identity preserving loss aims to help identity information recovery .
The procedure allows the incorporation of the more general projective camera motion model into the framework , instead of only displacement and rotational models . Several simulations that compare the implementations of the algorithm presented here with other related approaches help illustrate the suitability of SGWs ( coupled with hard or soft thresholding ) in the task of image sequence superresolution with simultaneous noise filtering . © 2004
Explicit inter-frame pixel correspondences are not required for our super-resolution procedure , thus we can avoid a huge computation time and obtain improved depth map in the accuracy and resolution as well as high-resolution images with reasonable time . The superiority of our algorithm is demonstrated by presenting the improved depth map accuracy , image super-resolution results , and camera pose estimation .
For existing methods , their denoising performance does not differ much , while our model achieves 0.38dB , 0.47dB , 0.49dB and 0.42dB higher of PSNR over WNNM . subsection : Image super - resolution The evaluation on Set5 is shown in Table [ reference ] .
WuzhenShi , FengJiang , DebinZhao SchoolofComputerScienceandTechnology , HarbinInstituteofTechnology , Harbin150001 , China Image super - resolution , convolutional neural network , multi - scale information , dilated convolution , inception module section : Introduction
section : Introduction In the problem of single - image super - resolution ( SR ) , the aim is to recover a high - resolution ( HR ) image from a single low - resolution ( LR ) image . In recent years , SR performance has been significantly improved due to rapid developments in deep neural networks ( DNNs ) .
section : Introduction Single image super - resolution ( SISR ) aims to recover a high - resolution ( HR ) version of a low - resolution ( LR ) input . As a classical problem , SISR is still an active yet challenging research topic in the field of computer vision due to its ill - poseness nature and high practical values .
section : Introduction Single image super - resolution ( SR ) aims at reconstructing a high - resolution ( HR ) image by restoring the high frequencies details from a single low - resolution ( LR ) image . SR is heavily ill - posed since multiple HR patches could correspond to the same LR image patch .
Let be the input LR image and be the set of network parameters to be optimized . Our goal is to learn a mapping function for generating a high - resolution image that is close to the ground truth HR image . We denote the residual image at level by , the upscaled LR image by and the corresponding HR images by .
subsection : Image Super - Resolution Super - resolution is the process of recovering a high resolution image from a low resolution image while generating realistic and plausible details . Following PixelRecursiveSuperResolution , in our experimental setup we enlarge an pixel image four - fold to , a process that is massively underspecified : the model has to generate aspects such as texture of hair , makeup , skin and sometimes even gender that can not possibly be recovered from the source image .
paragraph : Downsampling Input Image resolution is the most critical factor that affects running speed as analyzed in Sec . [ reference ] .
3 ) We demonstrate that deep learning is useful in the classical computer vision problem of superresolution , and can achieve good quality and speed . A preliminary version of this work was presented earlier
In particular , it can readily deal with three channels simultaneously by setting the input channels to c = 3 . In the following experiments , we explore different training strategies for color image super - resolution , and subsequently evaluate their performance on different channels . Implementation details .
To make the best use of the previous estimations and shared redundancy across the consecutive video frames , here we propose a scene and class agnostic , fully convolutional neural network model for 4 × video super-resolution . One stage of our network is composed of a motion compensation based input subnetwork , a blending backbone , and a spatial upsampling subnetwork .
A second obstacle is the non-differentiability and the non-convexity of some of the terms of the cost function : the non-differentiability stems from the use of regularization terms of the state of the art ( e.g. , to enforce sparsity ) whereas the non-convexity appears as soon as the motion describing the video is unknown . In this paper , we propose an overall algorithmic framework to address the video super-resolution problem . Our approach is based on fast gradient evaluation methods and modern optimization techniques for non-differentiable/non-convex problems .
Multi-frame super resolution has been well studied in recent years , but blur kernel is always assumed to be known in video super resolution problem . Most blind deconvolution algorithm can both estimate the blur kernel and the sharp image .
To stabilize the problem and make its solution more accurate , we develop two video super-resolution techniques : 1 ) a 2D autoregressive modeling and interpolation tech­ nique for video super-resolution reconstruction , with model parameters estimated from multiple registered low-resolution frames ; 2 ) the use of image model as a regu­ larization term to improve the performance of the traditional video super-resolution algorithm . We further investigate the interactions of various unknown variables involved in video super-resolution reconstruction , including motion parameters , high-resolution pixel intensities and the parameters of the image model used for regularization . We succeed in developing a joint estimation technique that infers these unknowns simul­ taneously to achieve statistical consistency among them .
Regularization-based and a fast non-iterative methods using optical flow estimation are suggested for video data super-resolution with correction of non-uniform illumination .
This paper introduces a novel large dataset for video deblurring , video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges . The video deblurring and video super-resolution challenges are each the first challenge of its kind , with 4 competitions , hundreds of participants and tens of proposed solutions . Our newly collected REalistic and Diverse Scenes dataset ( REDS ) was employed by the challenges .
Video super-resolution enhancement technique has two main aims . The first is reinforcing the shortage of video device frame rate , describing the details of fast-moving object .
Abstract . Video-frame registration is a critical step in the video super-resolution process . However , many existing registration methods can handle only small local neighborhood movements or overall affine transformations .
Abstract In view of the common existing problems in present video-to-video super-resolution reconstruction , this paper proposes a pioneering video-to-video super-resolution reconstruction algorithm based on segmentation and space – time regularisation to solve these problems . First , a video-to-video super-resolution reconstruction algorithm based on segmentation is proposed to eliminate reconstructed temporal ringing and to improve the times of reconstruction .
document : Real - Time Single Image and Video Super - Resolution Using an Efficient Sub - Pixel Convolutional Neural Network Recently , several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super - resolution .
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 . In this paper , we introduce spatio - temporal sub - pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real - time speed .
This brings Amdhal 's law in effect with full force , so that message-passing latency and bandwidth severely restrict performance . The authors examine an application from this domain , stereo image processing , which has been implemented in Adapt , a niche language for parallel image processing implemented on the Carnegie Mellon-Intel Corporation iWarp . High performance has been achieved for this application .
In this paper , stereo image reconstruction using regularized adaptive disparity estimation is proposed . That is , by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm , the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors .
The MATLAB software can do reconstruction of any object if it has two images from two different positions . The stereo image reconstruction it 's a very demanded task , due to the fact that 3D imaging became more popular in the last years . The 3D object than it can be used as a normal 3D generated object , the only difference is that it 's much simpler to make , it needs only the images from different sides of the object and with the usage of some image processing software the 3D image can be generated .
With the popularity of dual cameras in recently released smart phones , a growing number of super-resolution ( SR ) methods have been proposed to enhance the resolution of stereo image pairs . However , the lack of high-quality stereo datasets has limited the research in this area .
In this paper , we present a method for stereo super-resolution which employs a deep network . The network is trained using the residual image so as to obtain a high resolution image from two , low resolution views .
Stereo vision range sensors have been used for several decades to obtain 3D information of the scene . Still , the major issue involved in the stereo analysis of images is the correspondence problem . In this paper we consider the correspondence problem only for point clouds .
A new multistage adaptive robust ( MAR ) algorithm combined with a multiresolution coarse to fine matching model is developed for robust stereo analysis . Stereo analysis of remotely sensed images is useful for a variety of applications including cloud height measurement and digital terrain models . Time sequential stereoscopic observations of clouds from meteorological satellites provide a basic analysis tool for a broad spectrum of applications including numerical weather prediction , cloud modeling , and global climate understanding .
The system includes one to five videotape color CCD video cameras , one calibration color CCD video camera , two to eight projectors , one large screen display terminal , one calibration plate , and the compute set multi-path synchronization image acquisition card by which it is connected with the video camera , and multi-path output display card by which it is connected with the projector . The method includes the steps that image source video frequency coding , projector calibration , stereo image mosaicking . The invention can realize large screen vivid three-dimensional multi-projection display based on computer vision technology .
This paper demonstrates a gesture recognition approach based on binocular camera . The binocular vision system can deal with stereo imaging problem using disparity map . After the cameras are calibrated , the approach uses skin color model and depth information to separate the hand from the environment in the image .
The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages , sent between the distributed computers . We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery , and show that inference is possible with more than 200 labels in images larger than 10 MPixels .
Advancements over the last decade in video acquisition and display technologies lead to a continuous increase of video content resolution . These aspects combined with the shift towards cloud multimedia services and the underway adoption of High Efficiency Video Coding standards ( HEVC ) create a lot of interest for Super-Resolution ( SR ) and video enhancing techniques . Recent works showed that proximal based convex optimization approaches provide a promising direction in video restoration .
Kernel regression based super-resolution approaches are promising , but kernel selection is a critical problem . In order to avoid demanding and time-consuming cross validation for kernel selection , we propose multi-kernel regression ( MKR ) model for image Super-Resolution ( SR ) . Considering the multi-kernel regression model is prohibited when the training data is large-scale , we further propose a prototype MKR algorithm which can reduce the computational complexity .
Face Super-Resolution ( SR ) is a subfield of the SR domain that specifically targets the reconstruction of face images . The main challenge of face SR is to restore essential facial features without distortion .
In many applications , a high-resolution image is desired from available multiple degraded image frames , especially in surveillance recording . This paper represents a new objet based Super-Resolution using Phase Template Matching . This is a problem of extracting an object from a series of images which extracted image has more high frequency information than any one of the input images .
The present work extends the scope of Optical Super-Resolution to imaging systems with spatially-varying blur , by using sinusoidal illumination . It also establishes that knowledge of the space-variant blur is not a pre-requisite for super-resolution .
Image Super-Resolution : Historical Overview and Future Challenges , J. Yang and T. Huang Introduction to Super-Resolution Notations Techniques for Super-Resolution Challenge issues for Super-Resolution Super-Resolution Using Adaptive Wiener Filters , R.C. Hardie Introduction Observation Model AWF SR Algorithms Experimental Results Conclusions Acknowledgments Locally Adaptive Kernel Regression for Space-Time Super-Resolution , H. Takeda and P. Milanfar Introduction Adaptive Kernel Regression Examples Conclusion AppendiX Super-Resolution With Probabilistic Motion Estimation , M. Protter and M. Elad Introduction Classic Super-Resolution : Background The Proposed Algorithm Experimental Validation Summary Spatially Adaptive Filtering as Regularization in Inverse Imaging , A. Danielyan , A. Foi , V. Katkovnik , and K. Egiazarian Introduction Iterative filtering as regularization Compressed sensing Super-resolution Conclusions Registration for Super-Resolution , P. Vandewalle , L. Sbaiz , and
However , they require thousands of gradient updates , i.e. , long inference time . In this paper , we present Meta-Transfer Learning for Zero-Shot Super-Resolution ( MZSR ) , which leverages ZSSR . Precisely , it is based on finding a generic initial parameter that is suitable for internal learning .
Aiming at robust face recognition , this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly . Possible solutions to the problem are discussed and grouped into three categories : gray-scale resolution invariant features , gray-scale degradation modeling and Gray-scale Super-Resolution ( GSR ) . Then , we propose a Coupled Subspace Analysis ( CSA ) based GSR method to recover the high gray-scale resolution image from a single input LGR image .
Single Image Super-Resolution task based on GANs has shown a great improvement in all methods , but still has the optimization problems of texture details and distortion of local regions in super-resolved images . In this paper , we proposed an attention-based GAN architecture to solve preceding problems .
We propose a two-step neural approach for face Super-Resolution ( SR ) to improve face recognition performance . It consists in first performing generic SR on the entire image , based on Convolutional Neural Networks , followed by a specific local SR step for each facial component , using neural autoencoders .
Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image An improved super resolution reconstruction method based on initial value estimation Assessment method of image super resolution reconstruction based on local similarity Wavelet-domain HMT-based image super-resolution Image super-resolution restoration based on adaptive regularization MAP Wavelet Domain Generative Adversarial Network for Multi-scale Face Hallucination A second‐generation wavelet framework for super‐resolution with noise filtering Simultaneous Super-Resolution of Depth and Images Using a Single Camera Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Single image super-resolution with dilated convolution based multi-scale information learning inception module Image Super-Resolution via Dual-State Recurrent Networks Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Seven Ways to Improve Example-Based Single Image Super Resolution Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Image Transformer ICNet for Real-Time Semantic Segmentation on High-Resolution Images Image Super-Resolution Using Deep Convolutional Networks MultiBoot Vsr: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution Estimation of Super-Resolved Video Dynamics Fast deblurring in video super resolution Model-based Regularization for Video Super-Resolution Fast super-resolution from video data using optical flow estimation NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study Survey on Video Super-Resolution Enhancement Combining coarse and fine registration for video frame super-resolution reconstruction A pioneering video-to-video super-resolution reconstruction algorithm based on segmentation and space–time regularisation 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 Latency and bandwidth considerations in parallel robotics image processing Stereo image reconstruction using regularized adaptive disparity estimation 3D Image Reconstruction of a Robotic Arm in MATLAB from Images Acquired with FPGA Flickr1024: A Dataset for Stereo Image Super-Resolution Stereo Super-Resolution via a Deep Convolutional Network Fast correspondence search algorithm for stereo vision range sensors Robust stereo analysis Free multi visul point polyprojecting 3D displaying system and method A Gesture Recognition Method Based on Binocular Vision System Distributed message passing for large scale graphical models A study of norms in convex optimization super-resolution from compressed sources Image super-resolution base on multi-kernel regression Progressive Face Super-Resolution via Attention to Facial Landmark A novel object based Super-Resolution using Phase Template Matching Space-Variant Optical Super-Resolution using Sinusoidal Illumination Super-resolution imaging Meta-Transfer Learning for Zero-Shot Super-Resolution Gray-scale super-resolution for face recognition from low Gray-scale resolution face images Attention-Based GAN for Single Image Super-Resolution Boosting face recognition via neural Super-Resolution
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Patch-MI@MICCAI ICIMCS ICIP (2) CVPR NIPS 2017 IEEE International Conference on Image Processing (ICIP) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICML ECCV IEEE Transactions on Pattern Analysis and Machine Intelligence CVPR Workshops CVPR Workshops 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SC DICTA I2MTC ICVS CVPR MMSP ICIP IGTA ESANN
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