We introduce a new system for automatic image content removal and inpainting. Unlike traditional inpainting algorithms, which require advance knowledge of the region to be filled in, our system automatically detects the area to be removed and infilled. Region segmentation and inpainting are performed jointly in a single pass. In this way, potential segmentation errors are more naturally alleviated by the inpainting module. The system is implemented as an encoder-decoder architecture, with two decoder branches, one tasked with segmentation of the foreground region, the other with inpainting. The encoder and the two decoder branches are linked via neglect nodes, which guide the inpainting process in selecting which areas need reconstruction. The whole model is trained using a conditional GAN strategy. Comparative experiments show that our algorithm outperforms state-of-the-art inpainting techniques (which, unlike our system, do not segment the input image and thus must be aided by an external segmentation module.)
Image inpainting @cite_31 has a long history. The goal of inpainting is to fill in a missing region with realistic image content. A variety of inpainting methods have been proposed, including those based on prior image statistics @cite_23 @cite_0 , and those based on CNNs @cite_21 @cite_4 . More recently, outstanding results have been demonstrated with the use of GANs to inpaint even large missing areas @cite_8 @cite_2 @cite_15 @cite_35 . While appropriate for certain domains ( face inpainting), methods in this category often suffer from serious limitations, including the requirement that the missing region have fixed size and shape. All of the inpainting methods mentioned above assume that the corrupted region mask is known (typically as provided by the user). This limits their scope of application, as in most cases, this mask is unavailable. This shortcoming is addressed by blind inpainting algorithms @cite_17 @cite_30 , which do not need access to the foreground mask. However, prior blind inpainting work has been demonstrated only for very simple cases, with constant-valued foreground occupying a small area of the image.
@cite_30 @cite_35 @cite_4 @cite_8 @cite_15 @cite_21 @cite_0 @cite_23 @cite_2 @cite_31 @cite_17
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Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks. However, current network solutions still introduce undesired artifacts and noise to the repaired regions. We present an image inpainting method that is based on the celebrated generative adversarial network (GAN) framework. The proposed PGGAN method includes a discriminator network that combines a global GAN (G-GAN) architecture with a patchGAN approach. PGGAN first shares network layers between G-GAN and patchGAN, then splits paths to produce two adversarial losses that feed the generator network in order to capture both local continuity of image texture and pervasive global features in images. The proposed framework is evaluated extensively, and the results including comparison to recent state-of-the-art demonstrate that it achieves considerable improvements on both visual and quantitative evaluations. Abstract Although image inpainting is now an effective image editing technique, limited work has been done for inpainting forensics. The main drawbacks of the conventional inpainting forensics methods lie in the difficulties on inpainting feature extraction and the very high computational cost. In this paper, we propose a novel approach based on a convolutional neural network (CNN) to detect patch-based inpainting operation. Specifically, the CNN is built following the encoder–decoder network structure, which allows us to predict the inpainting probability for each pixel in an image. To guide the CNN to automatically learn the inpainting features, a label matrix is generated for the CNN training by assigning a class label for each pixel of an image, and the designed weighted cross-entropy serves as the loss function. They further help to strongly supervise the CNN to capture the manipulation information rather than the image content features. By the established CNN, inpainting forensics does not need to consider feature extraction and classifier design, and use any postprocessing as in conventional forensics methods. They are combined into the unique framework and optimized simultaneously. Experimental results show that the proposed method achieves superior performance in terms of true positive rate, false positive rate and the running time, as compared with state-of-the-art methods for inpainting forensics, and is very robust against JPEG compression and scaling manipulations. Most inpainting approaches require a good image model to infer the unknown pixels. In this work, we directly learn a mapping from image patches, corrupted by missing pixels, onto complete image patches. This mapping is represented as a deep neural network that is automatically trained on a large image data set. In particular, we are interested in the question whether it is helpful to exploit the shape information of the missing regions, i.e. the masks, which is something commonly ignored by other approaches. In comprehensive experiments on various images, we demonstrate that our learning-based approach is able to use this extra information and can achieve state-of-the-art inpainting results. Furthermore, we show that training with such extra information is useful for blind inpainting, where the exact shape of the missing region might be uncertain, for instance due to aliasing effects. In this paper, we propose a novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network (DCGAN). We define a loss function consisting of two parts: (1) a contextual loss that preserves similarity between the input corrupted image and the recovered image, and (2) a perceptual loss that ensures a perceptually realistic output image. Given a corrupted image with missing values, we use back-propagation on this loss to map the corrupted image to a smaller latent space. The mapped vector is then passed through the generative model to predict the missing content. The proposed framework is evaluated on the CelebA and SVHN datasets for two challenging inpainting tasks with random 80 corruption and large blocky corruption. Experiments show that our method can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods. Given an image where a specified region is unknown, image inpainting or image completion is the problem of inferring the image content in this region. Traditional retouching or inpainting is the practice of restoring aged artwork, where damaged or missing portions are repainted based on the surrounding content to approximate the original appearance. In the context of digital images, inpainting is used to restore regions of an image that are corrupted by noise or where the data is missing. Inpainting is also used to solve disocclusion, to estimate the scene behind an obscuring foreground object. A popular use of digital inpainting is object removal, for example, to remove a trashcan that disrupts a scene of otherwise natural beauty. Inpainting is an interpolation problem, filling the unknown region with a condition to agree with the known image on the boundary. A classical solution for such an interpolation is to solve Laplace’s equation. However, Laplace’s equation is usually unsatisfactory for images since it is overly smooth. It cannot recover a step edge passing through the region. Total variation (TV) regularization is an effective inpainting technique which is capable of recovering sharp edges under some conditions (these conditions will be explained). The use of TV regularization was originally developed for image denoising by Rudin, Osher, and Fatemi [3] and then applied to inpainting by Chan and Shen [13]. TV-regularized inpainting does not create texture, the method is limited to inpainting the geometric structure. Image inpainting has been widely used in practice to repair damaged missing pixels of given images. Most of the existing inpainting techniques require knowing beforehand where those damaged pixels are, either given as a priori or detected by some pre-processing. However, in certain applications, such information neither is available nor can be reliably pre-detected, e.g. removing random-valued impulse noise from images or removing certain scratches from archived photographs. This paper introduces a blind inpainting model to solve this type of problems, i.e., a model of simultaneously identifying and recovering damaged pixels of the given image. A tight frame based regularization approach is developed in this paper for such blind inpainting problems, and the resulted minimization problem is solved by the split Bregman algorithm rst proposed by [1]. The proposed blind inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches and removing image noise mixed with both Gaussian and random-valued impulse noise. The experiments show that our method is compared favorably against many available two-staged methods in these applications. We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shapes, global and local GANs designed for a single rectangular mask are not suitable. To this end, we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches. It is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. We show that our system helps users quickly remove distracting objects, modify image layouts, clear watermarks, edit faces and interactively create novel objects in images. Furthermore, visualization of learned feature representations reveals the effectiveness of gated convolution and provides an interpretation of how the proposed neural network fills in missing regions. More high-resolution results and video materials are available at this http URL In this paper, we propose a new video inpainting method which applies to both static or free-moving camera videos. The method can be used for object removal, error concealment, and background reconstruction applications. To limit the computational time, a frame is inpainted by considering a small number of neighboring pictures which are grouped into a group of pictures (GoP). More specifically, to inpaint a frame, the method starts by aligning all the frames of the GoP. This is achieved by a region-based homography computation method which allows us to strengthen the spatial consistency of aligned frames. Then, from the stack of aligned frames, an energy function based on both spatial and temporal coherency terms is globally minimized. This energy function is efficient enough to provide high quality results even when the number of pictures in the GoP is rather small, e.g. 20 neighboring frames. This drastically reduces the algorithm complexity and makes the approach well suited for near real-time video editing applications as well as for loss concealment applications. Experiments with several challenging video sequences show that the proposed method provides visually pleasing results for object removal, error concealment, and background reconstruction context. Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods. Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods. A framework for inpainting missing parts of a video sequence recorded with a moving or stationary camera is presented in this work. The region to be inpainted is general: It may be still or moving, in the background or in the foreground, it may occlude one object and be occluded by some other object. The algorithm consists of a simple preprocessing stage and two steps of video inpainting. In the preprocessing stage, we roughly segment each frame into foreground and background. We use this segmentation to build three image mosaics that help to produce time consistent results and also improve the performance of the algorithm by reducing the search space. In the first video inpainting step, we reconstruct moving objects in the foreground that are "occluded" by the region to be inpainted. To this end, we fill the gap as much as possible by copying information from the moving foreground in other frames, using a priority-based scheme. In the second step, we inpaint the remaining hole with the background. To accomplish this, we first align the frames and directly copy when possible. The remaining pixels are filled in by extending spatial texture synthesis techniques to the spatiotemporal domain. The proposed framework has several advantages over state-of-the-art algorithms that deal with similar types of data and constraints. It permits some camera motion, is simple to implement, fast, does not require statistical models of background nor foreground, works well in the presence of rich and cluttered backgrounds, and the results show that there is no visible blurring or motion artifacts. A number of real examples taken with a consumer hand-held camera are shown supporting these findings Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach. Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach. This paper describes a novel inpainting algorithm that is capable of filling in holes in overlapping texture and cartoon image layers. This algorithm is a direct extension of a recently developed sparse-representation-based image decomposition method called MCA (morphological component analysis), designed for the separation of linearly combined texture and cartoon layers in a given image (see [J.-L. Starck, M. Elad, D.L. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Trans. Image Process. (2004), in press] and [J.-L. Starck, M. Elad, D.L. Donoho, Redundant multiscale transforms and their application for morphological component analysis, Adv. Imag. Electron Phys. (2004) 132]). In this extension, missing pixels fit naturally into the separation framework, producing separate layers as a by-product of the inpainting process. As opposed to the inpainting system proposed by , where image decomposition and filling-in stages were separated as two blocks in an overall system, the new approach considers separation, hole-filling, and denoising as one unified task. We demonstrate the performance of the new approach via several examples. Inpainting in hyperspectral imagery is a challenging research area and several methods have been recently developed to deal with this kind of data. In this paper we address missing data restoration via a convex optimization technique with regularization term based on Collaborative Total Variation (CTV). In particular we evaluate the effectiveness of several instances of CTV in conjunction with different dimensionality reduction algorithms. Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feedforward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones. Code, demo and models are available at: https: github.com JiahuiYu generative_inpainting. Many 3D reconstruction methods produce incomplete depth maps. Depth map inpainting can generate visually plausible structures for the missing areas. We present an inpainting method that encourages flat surfaces without favouring fronto-parallel planes. Moreover, it uses a color image to guide the inpainting and align color and depth edges. We implement the algorithm efficiently through graph-cuts. We compare the performance of our method with another inpainting approach used for large datasets and we show the results using several datasets. The depths inpainted with our method are visually plausible and of higher quality. In recent years, we have witnessed the great success of deep learning on various problems both in low and high-level computer visions. The low-level vision problems, including inpainting, deblurring, denoising, super-resolution, and so on, are highly anticipated to occur in machine vision and image processing. Many deep learning based methods have been proposed to solve low-level vision problems. Most researches treat these problems independently; however, most of the time they appear concurrently. Motivated by the success of generative model in the field of image generation, we develop a deep cascade of neural networks to solve the inpainting, deblurring, denoising problems at the same time. Our model contains two networks: inpainting GAN and deblurring-denoising network. Inpainting GAN generates the coarse patches to fill the lost part in damaged image, and the deblurring-denoising network, stacked by a convolutional auto-encoder, will further refine them. Unlike other methods that handle each problem separately, our method jointly optimizes the two sub-networks. Because GAN training is not only unstable but also difficult, we adopt the Wasserstein distance as the loss function of the inpainting GAN and propose a gradual training strategy. Learning from the idea of residual learning, we utilize skip connections to pass image details from input to reconstruction layer. Experimental results have demonstrated that the proposed model can achieve state-of-the-art performance. Through the experiments, we also demonstrated the effectiveness of the cascade architecture. We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods. Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information on images to solve this problem. These algorithms successfully solve the local inpainting problem but they must, by definition, give the same completion to any two holes that have the same boundary, even when the rest of the image is vastly different. We address a different, more global inpainting problem. How can we use the rest of the image in order to learn how to inpaint? We approach this problem from the context of statistical learning. Given a training image we build an exponential family distribution over images that is based on the histograms of local features. We then use this image specific distribution to inpaint the hole by finding the most probable image given the boundary and the distribution. The optimization is done using loopy belief propagation. We show that our method can successfully complete holes while taking into account the specific image statistics. In particular it can give vastly different completions even when the local neighborhoods are identical.