Image inpainting is the process of repairing damaged or missing areas an image by utilizing information from its surrounding environment. The fractional order operator, which processes details like textures and edges with a finer scale, has demonstrated better outcomes success in processing. This study draws inspiration definition singular integral Laplacian presents two adaptive variational fu...

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial “sparse” methodology. We derive a large scale approximate Bayesian inference algorithm for linear models with nonfact...

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method’s performance in the image denoising task is comparable t...

Image inpainting refers to the process of changing an image so that an observer seeing the change would not guess that the image was intended to look any other way. It usually refers to the task of restoring damaged images and is considered an art for the careful artist’s eye it takes to get all the details right. This work implements an algorithm for automatically inpainting a user-specified r...

Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have shown to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in curren...

Inpainting-based image compression is an emerging paradigm for compressing visual data in a completely different way than popular transform-based methods such as JPEG. The underlying idea sounds very simple: One stores only a small, carefully selected subset of the data, which results in a substantial reduction of the file size. In the decoding phase, one interpolates the missing data by means ...

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image ...

We propose a total variation based model for simultaneous image inpainting and blind deconvolution. We demonstrate that the tasks are inherently coupled together and that solving them individually will lead to poor results. The main advantages of our model are that (i) boundary conditions for deconvolution required near the interface between observed and occluded regions are naturally generated...

Image completion is one of the subjects in image and video processing which deals with restoration of and filling in damaged regions of images using correct regions. Exemplar-based image completion methods give more pleasant results than pixel-based approaches. In this paper, a new algorithm is proposed to find the most suitable patch in order to fill in the damaged parts. This patch selection ...