Image inpainting is a technique to repair damaged images or modify images in a non-detectable form . In this paper , a novel global algorithm for region filling is proposed for image inpainting . We experiment with natural images of faces , flowers , and birds , and demonstrate that the proposed models are capable of generating realistic and diverse samples with fine-grained category labels . We further show that our models can be applied to other tasks , such as image inpainting , super-resolution , and data augmentation for training better face recognition models . In particular , partial differential equations ( PDEs ) are widely used and are proved to be efficient . If we denote by Ω the entire image domain , the image inpainting problem is then to fill in image information in the incomplete/damaged region D ⊂ Ω based on the image information available out side D , i.e. , in Ω\D. When this information is available in a neighborhood of the whole boundary ∂D , it can be used as a Dirichlet boundary condition for the partial differential equation that propagates the information inside D. The aim of this work is to treat the case when this information is not available near a part of the boundary ∂D. Image inpainting in this case is considered as a linear Cauchy problem for the harmonic inpainting , and a nonlinear Cauchy problem for images containing edges . The novelty of this work has two folds . Various approaches have been used including the generative model using deep neural network , modified inputs for the generator , application of pre-trained classification model , etc . In this paper , the advantages of different GANs are combined to tackle the image inpainting problem . Besides , this paper proposed a special data feeding approach which concurrently trains generator and discriminator of a GAN to further improve the performance of domain specific inpainting tasks . Abstract This paper proposed a new image inpainting method for restoring a large damaged area in the images called the “ geometric image inpainting ( GII ) method ” . The method was developed in response to several common problems encountered in the image inpainting process and provided a high-quality result compared to human vision . In the field of image inpainting , there are three key issues : texture analysis , repairing order and reconstruction of lost image pixels . One of the most well-known and fundamental algorithms in this field has been proposed by Criminisi . In spite of the fact that the Criminisi 's algorithm is fast , simple and makes effort to propagate both image textures and structures , it fails when it comes to inpainting images with convoluted structures . To tackle this issue , in this paper two improvements to the Criminisi 's algorithm have been suggested . Several approaches used for inpainting of images take advantage of sparse representations . Some of these seek to learn a dictionary that will adapt the sparse representation to the available data . Inpainting of images , represented on surfaces , was previously considered successfully by the application of a two-dimensional Laplacian operator , including in the context of the discrete representation on surfaces . Considering the shortcoming of errors and computational complexity of the strictly-two-dimensional approaches , we propose a one-dimensional-based multi-line approach , wherein the set of lines cover the region of inpainting and its boundaries . FCM algorithm classifies the image pixels into a number of categories according to the similarity principle , making the similar pixels clustering into the same category as possible . According to the provided gray value of the pixels to be inpainted , we calculate the category whose distance is the nearest to the inpainting area and this category is to be inpainting area , and then the inpainting area is restored by the TV model to realize image automatic inpainting . This paper studies an image inpainting solution based on a primal sketch representation model [ 1 ] , which divides an image into structure ( sketchable ) and texture ( non-sketchable ) components . This solution first predicts the missing structures , such as curves and corners , using a tensor voting algorithm [ 2 ] . The temporal structure inference network is built upon a 3D fully convolutional architecture : it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution . The low resolution result provides temporal guidance to the spatial detail recovering network , which performs imagebased inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution . Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames . Several other papers have also used GANs for image - to - image mappings , but only applied the GAN unconditionally , relying on other terms ( such as L2 regression ) to force the output to be conditioned on the input . These papers have achieved impressive results on inpainting , future state prediction , image manipulation guided by user constraints , style transfer , and superresolution . Each of the methods was tailored for a specific application . Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art . In the variable splitting algorithms for general image restoration tasks , the prior term involves a denoising subproblem with a current noise level . Thus , the non - blind model can be easily plugged into variable splitting algorithms to solve various image restoration tasks , such as image deblurring , SISR , and image inpainting . However , the blind model does not have this merit . We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework . The approach models images as a collage of patches of arbitrary size , some of which contain the object of interest and some of which are background . When browsing through a book on computer graphics , one usually finds a lot of more or less interesting pictures that are produced by means of computers . These pictures are embedded in pages of technical texts describing how this image generation was performed and why it provides a better way to do so than other methods . Less space is usually given to the methodological background and the motivation underlying the preoccupation with computer visualization . The conventional approach is to pre-process the images to extract point observations and then perform tracking , i.e. infer the target trajectories . We model the image generation process directly to avoid any potential loss of information when extracting point observations using a pre-processing step that is decoupled from the inference algorithm . Numerical examples show that our algorithm has improved tracking performance over commonly used techniques , for both synthetic examples and real florescent microscopy data , especially in the case of dim targets with overlapping illuminated regions . Recently , three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding , object reconstruction as well as computer vision for robotic systems . The image generation problem and the model generation problem are presented . More specifically , the inputs to the image generator are an old image , object model , motion specification , and hidden line and hidden surface algorithms . Deep learning algorithm display powerful ability in Computer Vision area , in recent year , the CNN has been applied to solve problems in the subarea of Image-generating , which has been widely applied in areas such as photo editing , image design , computer animation , real-time rendering for large scale of scenes and for visual effects in movies . However in the texture synthesize procedure . This motivates our new discrepancies , introduced in Section 3 . Section 4 demonstrates that these losses outperform state - of - the - art models for image generation . section : Learning implicit generative models with MMD - based losses We first review CNN applied to image data and then discuss the application of CNN to document classification tasks to introduce seq - CNN and bow - CNN . subsection : Preliminary : CNN for image CNN is a feed - forward neural network with convolution layers interleaved with pooling layers , as illustrated in Figure [ reference ] , where the top layer performs classification using the features generated by the layers below . The simplest form that this takes ( and which has very tight relationships with common attention models and kernel functions ) is to use the softmax over the cosine distance , i.e. , with embedding functions and being appropriate neural networks ( potentially with ) to embed and . In our experiments we shall see examples where and are parameterised variously as deep convolutional networks for image tasks ( as in VGG or Inception ) or a simple form word embedding for language tasks ( see Section [ reference ] ) . We note that , though related to metric learning , the classifier defined by Equation [ reference ] is discriminative . This eliminates the need to perform most of the SR operation in the far larger HR resolution . For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network . The non - parametric models often do matching from a database of existing images , often matching patches of images , and have been used in texture synthesis efros1999texture , super - resolution freeman2002example and in - painting hays2007scene . Parametric models for generating images has been explored extensively ( for example on MNIST digits or for texture synthesis portilla2000parametric ) . However , generating natural images of the real world have had not much success until recently . The experimental results show that APIDCT and APDST filters with a size of 7 × 7 are similar in interpolation performance , but better than the bilinear interpolation method . In addition to its use in image interpolation demosaicking , the low-pass filter designed in this paper can also be widely used in image interpolation , image denoising , image resizing , and other fields of image processing . Inpainting is an image interpolation problem with broad applications in image processing and vision analysis . In our research , a new algorithm is proposed for removing objects from digital images and filling in the hole that is left behind in a visually plausible way . Experimental results confirm the effectiveness of the proposed algorithm . Keywords– Image inpainting , image interpolating , image reconstruction , bilateral filtering The main idea of the new method is to combine PSO algorithm with wavelet multiresolution strategy and parameters of PSO are adapted along with the resolution of the images . The experiments show that this method can avoid registration process to get stuck into local extremes in image interpolation operations and finds the optimal exchange through limited iterations computation , and obtain subpixel registration accuracy in process of image stitching . Secondly , to solve the problem of image blur stitching when the geometric deformation and the changes of the scale factor among images are serious , a new method based robust features matching is proposed in this thesis . A GRAMM allows us to create virtual images , by interpolating between sets of images taken at finitely sampled positions in location and in time . This interpolation requires algorithms for estimation of 3-D scene motion , image based interpolation , and imposition of multi-camera geometric constraints . Applications include visualization of complex surgical procedures , entertainment , or training , where the physical locations and/or image capture rates of cameras are constrained , but there is a need to accurately reconstruct the appearance from any position , at any time instant . To tackle this problem , a simultaneous approach is proposed in this paper . Since the prior image model is important for solving the ill-posed numerical issues encountered in the image interpolation computation , the proposed approach exploits a prior image model that considers both the spatial local smoothness constraint within each reconstructed high-resolution image and the disparity-compensated local smoothness constraint between the pair of reconstructed high-resolution images . The proposed approach requires the disparity to be known in advance or has been accurately estimated by the existing stereo matching algorithm . In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process . Given the image under interpolation , four binary contrast-guided decision maps ( CDMs ) are generated and used to guide the interpolation filtering through two sequential stages : 1 ) the 45 ° and 135 ° CDMs for interpolating the diagonal pixels and 2 ) the 0 ° and 90 ° CDMs for interpolating the row and column pixels . By fundus fluoresce in angiography and the resulting digital image processing of their data obtained , reflect the structure of retinal blood vessels , hemodynamic changes , disease management and related physiological changes the structure of pathological changes , widely used in the retina , choroid and optic nerve disease in the differential diagnosis . Objectives : By analyzing the characteristics of the fundus angiography images and BP neural network , using BP neural network for fundus angiography is of image segmentation . Methods : Segmented image region will be divided into two types of background and objectives , obtained by these two types of manual methods of sample images , extracting the characteristics of the sample image , such as gray scale , variance , texture , etc .
Image inpainting is a technique to repair damaged images or modify images in a non-detectable form . In this paper , a novel global algorithm for region filling is proposed for image inpainting .
We experiment with natural images of faces , flowers , and birds , and demonstrate that the proposed models are capable of generating realistic and diverse samples with fine-grained category labels . We further show that our models can be applied to other tasks , such as image inpainting , super-resolution , and data augmentation for training better face recognition models .
In particular , partial differential equations ( PDEs ) are widely used and are proved to be efficient . If we denote by Ω the entire image domain , the image inpainting problem is then to fill in image information in the incomplete/damaged region D ⊂ Ω based on the image information available out side D , i.e. , in Ω\D. When this information is available in a neighborhood of the whole boundary ∂D , it can be used as a Dirichlet boundary condition for the partial differential equation that propagates the information inside D. The aim of this work is to treat the case when this information is not available near a part of the boundary ∂D. Image inpainting in this case is considered as a linear Cauchy problem for the harmonic inpainting , and a nonlinear Cauchy problem for images containing edges . The novelty of this work has two folds .
Various approaches have been used including the generative model using deep neural network , modified inputs for the generator , application of pre-trained classification model , etc . In this paper , the advantages of different GANs are combined to tackle the image inpainting problem . Besides , this paper proposed a special data feeding approach which concurrently trains generator and discriminator of a GAN to further improve the performance of domain specific inpainting tasks .
Abstract This paper proposed a new image inpainting method for restoring a large damaged area in the images called the “ geometric image inpainting ( GII ) method ” . The method was developed in response to several common problems encountered in the image inpainting process and provided a high-quality result compared to human vision . In the field of image inpainting , there are three key issues : texture analysis , repairing order and reconstruction of lost image pixels .
One of the most well-known and fundamental algorithms in this field has been proposed by Criminisi . In spite of the fact that the Criminisi 's algorithm is fast , simple and makes effort to propagate both image textures and structures , it fails when it comes to inpainting images with convoluted structures . To tackle this issue , in this paper two improvements to the Criminisi 's algorithm have been suggested .
Several approaches used for inpainting of images take advantage of sparse representations . Some of these seek to learn a dictionary that will adapt the sparse representation to the available data .
Inpainting of images , represented on surfaces , was previously considered successfully by the application of a two-dimensional Laplacian operator , including in the context of the discrete representation on surfaces . Considering the shortcoming of errors and computational complexity of the strictly-two-dimensional approaches , we propose a one-dimensional-based multi-line approach , wherein the set of lines cover the region of inpainting and its boundaries .
FCM algorithm classifies the image pixels into a number of categories according to the similarity principle , making the similar pixels clustering into the same category as possible . According to the provided gray value of the pixels to be inpainted , we calculate the category whose distance is the nearest to the inpainting area and this category is to be inpainting area , and then the inpainting area is restored by the TV model to realize image automatic inpainting .
This paper studies an image inpainting solution based on a primal sketch representation model [ 1 ] , which divides an image into structure ( sketchable ) and texture ( non-sketchable ) components . This solution first predicts the missing structures , such as curves and corners , using a tensor voting algorithm [ 2 ] .
The temporal structure inference network is built upon a 3D fully convolutional architecture : it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution . The low resolution result provides temporal guidance to the spatial detail recovering network , which performs imagebased inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution . Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames .
Several other papers have also used GANs for image - to - image mappings , but only applied the GAN unconditionally , relying on other terms ( such as L2 regression ) to force the output to be conditioned on the input . These papers have achieved impressive results on inpainting , future state prediction , image manipulation guided by user constraints , style transfer , and superresolution . Each of the methods was tailored for a specific application .
Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art .
In the variable splitting algorithms for general image restoration tasks , the prior term involves a denoising subproblem with a current noise level . Thus , the non - blind model can be easily plugged into variable splitting algorithms to solve various image restoration tasks , such as image deblurring , SISR , and image inpainting . However , the blind model does not have this merit .
We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework . The approach models images as a collage of patches of arbitrary size , some of which contain the object of interest and some of which are background .
When browsing through a book on computer graphics , one usually finds a lot of more or less interesting pictures that are produced by means of computers . These pictures are embedded in pages of technical texts describing how this image generation was performed and why it provides a better way to do so than other methods . Less space is usually given to the methodological background and the motivation underlying the preoccupation with computer visualization .
The conventional approach is to pre-process the images to extract point observations and then perform tracking , i.e. infer the target trajectories . We model the image generation process directly to avoid any potential loss of information when extracting point observations using a pre-processing step that is decoupled from the inference algorithm . Numerical examples show that our algorithm has improved tracking performance over commonly used techniques , for both synthetic examples and real florescent microscopy data , especially in the case of dim targets with overlapping illuminated regions .
Recently , three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding , object reconstruction as well as computer vision for robotic systems . The image generation problem and the model generation problem are presented . More specifically , the inputs to the image generator are an old image , object model , motion specification , and hidden line and hidden surface algorithms .
Deep learning algorithm display powerful ability in Computer Vision area , in recent year , the CNN has been applied to solve problems in the subarea of Image-generating , which has been widely applied in areas such as photo editing , image design , computer animation , real-time rendering for large scale of scenes and for visual effects in movies . However in the texture synthesize procedure .
This motivates our new discrepancies , introduced in Section 3 . Section 4 demonstrates that these losses outperform state - of - the - art models for image generation . section : Learning implicit generative models with MMD - based losses
We first review CNN applied to image data and then discuss the application of CNN to document classification tasks to introduce seq - CNN and bow - CNN . subsection : Preliminary : CNN for image CNN is a feed - forward neural network with convolution layers interleaved with pooling layers , as illustrated in Figure [ reference ] , where the top layer performs classification using the features generated by the layers below .
The simplest form that this takes ( and which has very tight relationships with common attention models and kernel functions ) is to use the softmax over the cosine distance , i.e. , with embedding functions and being appropriate neural networks ( potentially with ) to embed and . In our experiments we shall see examples where and are parameterised variously as deep convolutional networks for image tasks ( as in VGG or Inception ) or a simple form word embedding for language tasks ( see Section [ reference ] ) . We note that , though related to metric learning , the classifier defined by Equation [ reference ] is discriminative .
This eliminates the need to perform most of the SR operation in the far larger HR resolution . For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network .
The non - parametric models often do matching from a database of existing images , often matching patches of images , and have been used in texture synthesis efros1999texture , super - resolution freeman2002example and in - painting hays2007scene . Parametric models for generating images has been explored extensively ( for example on MNIST digits or for texture synthesis portilla2000parametric ) . However , generating natural images of the real world have had not much success until recently .
The experimental results show that APIDCT and APDST filters with a size of 7 × 7 are similar in interpolation performance , but better than the bilinear interpolation method . In addition to its use in image interpolation demosaicking , the low-pass filter designed in this paper can also be widely used in image interpolation , image denoising , image resizing , and other fields of image processing .
Inpainting is an image interpolation problem with broad applications in image processing and vision analysis . In our research , a new algorithm is proposed for removing objects from digital images and filling in the hole that is left behind in a visually plausible way .
Experimental results confirm the effectiveness of the proposed algorithm . Keywords– Image inpainting , image interpolating , image reconstruction , bilateral filtering
The main idea of the new method is to combine PSO algorithm with wavelet multiresolution strategy and parameters of PSO are adapted along with the resolution of the images . The experiments show that this method can avoid registration process to get stuck into local extremes in image interpolation operations and finds the optimal exchange through limited iterations computation , and obtain subpixel registration accuracy in process of image stitching . Secondly , to solve the problem of image blur stitching when the geometric deformation and the changes of the scale factor among images are serious , a new method based robust features matching is proposed in this thesis .
A GRAMM allows us to create virtual images , by interpolating between sets of images taken at finitely sampled positions in location and in time . This interpolation requires algorithms for estimation of 3-D scene motion , image based interpolation , and imposition of multi-camera geometric constraints . Applications include visualization of complex surgical procedures , entertainment , or training , where the physical locations and/or image capture rates of cameras are constrained , but there is a need to accurately reconstruct the appearance from any position , at any time instant .
To tackle this problem , a simultaneous approach is proposed in this paper . Since the prior image model is important for solving the ill-posed numerical issues encountered in the image interpolation computation , the proposed approach exploits a prior image model that considers both the spatial local smoothness constraint within each reconstructed high-resolution image and the disparity-compensated local smoothness constraint between the pair of reconstructed high-resolution images . The proposed approach requires the disparity to be known in advance or has been accurately estimated by the existing stereo matching algorithm .
In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process . Given the image under interpolation , four binary contrast-guided decision maps ( CDMs ) are generated and used to guide the interpolation filtering through two sequential stages : 1 ) the 45 ° and 135 ° CDMs for interpolating the diagonal pixels and 2 ) the 0 ° and 90 ° CDMs for interpolating the row and column pixels .
By fundus fluoresce in angiography and the resulting digital image processing of their data obtained , reflect the structure of retinal blood vessels , hemodynamic changes , disease management and related physiological changes the structure of pathological changes , widely used in the retina , choroid and optic nerve disease in the differential diagnosis . Objectives : By analyzing the characteristics of the fundus angiography images and BP neural network , using BP neural network for fundus angiography is of image segmentation . Methods : Segmented image region will be divided into two types of background and objectives , obtained by these two types of manual methods of sample images , extracting the characteristics of the sample image , such as gray scale , variance , texture , etc .
Image inpainting by global structure and texture propagation CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training Guest session — Image processing invited paper: A Cauchy problem for an inverse problem in image inpainting Domain specific inpainting with concurrently pretrained generative adversarial networks Image Inpainting Based On Geometric Similarity A new exemplar-based image inpainting algorithm using image structure tensors Task-driven dictionary learning for inpainting Inpainting of surfaces and Images An Automatic Image Inpainting Algorithm Based on FCM Automatic Image Inpainting by Heuristic Texture and Structure Completion Video Inpainting by Jointly Learning Temporal Structure and Spatial Details Image-to-Image Translation with Conditional Adversarial Networks Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising A generative framework for real time object detection and classification Abstraction versus realism: Not the real question Tracking multiple moving objects in images using Markov Chain Monte Carlo Algorithms for simple object reconstruction using the largest possible object approach CNN based texture synthesize with Semantic segment On gradient regularizers for MMD GANs Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Matching Networks for One Shot Learning Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Interpolation Filter Design Based on All-Phase DST and Its Application to Image Demosaicking Image Inpainting Algorithm Based on Successive Elimination A BILATERAL IMAGE INPAINTING The research on algorithm of image mosaic Multi-view Spatial and Temporal Interpolation for Dynamic Event Visualization Simultaneous image interpolation for stereo images Contrast-Guided Image Interpolation BP Neural Network in Fundus Angiography Image Segmentation
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2007 2017 2013 2017 2012 2016 2014 2018 2014 2010 2019 2017 2016 2018 2005 1998 2016 1992 2016 2018 2015 2016 2016 2016 2018 2009 2011 2008 1999 2013 2013 2011
ACM Multimedia GlobalSIP ICASSP MMM AAAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv IEEE Transactions on Image Processing NeurIPS HLT-NAACL NIPS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ACIS-ICIS research on algorithm of image mosaic /
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