Recovering lost part of an image plays a great role in image processing. Inpainting is a technique that helps in recovering lost pixels from an image. From the existing techniques of Inpainting, Exemplar Inpainting is one of the fast and better techniques that help in restoring the lost part of an image. Exemplar based method chooses a patch similar to the lost patch from the known area to fill...

The first contribution of this paper is the comparison of learned dictionary based approaches to inpainting and denoising of images in natural scenes, where emphasis is given on the use of complete and overcomplete dictionary learned by independent component analysis. The second contribution of the paper relates to the formulation of a problem of denoising an image corrupted by a salt and peppe...

Recovering lost part of an image plays a great role in image processing. Inpainting is a technique that helps in recovering lost pixels from an image. From the existing techniques of Inpainting, Exemplar Inpainting is one of the fast and better techniques that help in restoring the lost part of an image. Exemplar based method chooses a patch similar to the lost patch from the known area to fill...

In 2-D image compression, recent approaches based on image inpainting with edge-enhancing anisotropic diffusion (EED) rival the transform-based quasi-standards JPEG and JPEG 2000 and are even able to surpass it. In this paper, we extend successful concepts from these 2-D methods to the 3-D setting, thereby establishing the first PDE-based 3-D image compression algorithm. This codec uses a cuboi...

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 th...

Planar image processing has been widely investigated for many years. The processing operations include denoising, edge enhancement, edge detecting, inpainting, and others. But there exists little work about processing images on surfaces, since it is difficult to extend the classic methods to deal with the problem. In this paper, we study the inpainting algorithm of images on implicit surfaces b...

Image inpainting or image completion refers to the process of filling in the missing data of an image in a visually plausible way. Given a natural scene input image with selected target regions, both the geometrical property and texture information should be propagated from the known parts of image into the target regions. Exemplar-based model fills the target region using the source exemplars ...

Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectationmaximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, the inpainting/interpolation can be viewed as an estimation problem with missing data. Towards this...

We propose an automatic video inpainting algorithm which relies on the optimisation of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Furthermore, we achieve this in an order of magnitude less ...