One of the most important premises of research in image processing happens to be Image inpainting which is a technique to fill missing region or reconstruct damaged area in an image. Through image inpainting one can also remove an undesirable object from an image in visually plausible way. For filling the part of image, it uses information from the neighboring area. In this paper, we present an...

Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context. However, users cannot directly decide what content to synthesize with such approaches. We propose an end-to-end network for image inpainting that uses a different image to guide the synthesis of new content to fill the hole. A key challenge addressed by our approach is synthe...

Finding optimal inpainting data plays a key role in the field of image compression with partial differential equations (PDEs). In this paper, we optimise the spatial as well as the tonal data such that an image can be reconstructed with minimised error by means of discrete homogeneous diffusion inpainting. To optimise the spatial distribution of the inpainting data, we apply a probabilistic dat...

Object removal by image inpainting aims at the visual uniformity of the inpainted blanks among their surroundings. Most inpainting algorithms pursue the structure continuity and texture similarity only in color. In this paper we take the view depth continuity into account and propose a depth-guided inpainting algorithm, in which a single color image and its associated disparity map are inpainte...

Inpainting consists in computing a plausible completion of missing parts of an image 4 given the available content. In the restricted framework of texture images, the image can be seen as a 5 realization of a random field model, which gives a stochastic formulation of image inpainting: on the 6 masked exemplar one estimates a random texture model which can then be conditionally sampled in 7 ord...

Patch-based (or “pattern-based”) inpainting, is a popular processing technique aiming at reconstructing missing regions in images, by iteratively duplicating blocks of known image data (patches) inside the area to fill in. This kind of method is particularly effective to process wide image areas, thanks to its ability to reconstruct textured data. Nevertheless, “pathological” geometric configur...

—In the digital world, inpainting is the algorithm used to replace or reconstruct lost, corrupted, or deteriorated parts of image data. Of the various proposed inpainting methods, convolutional methods are the simplest and most efficient. In this paper, an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the...

In this paper, we propose a novel algorithm for image inpainting based on compactly supported radial basis functions (CSRBF) interpolation. The algorithm converts 2D image inpainting problem into implict surface reconstruction problem from 3D points set. Firstly, we construct the implicit surface for approximating the points set which convert from damaged image by using radial basis functions (...

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...

There are various real world situations where, a portion of the image is lost or damaged or hidden by an unwanted object which needs an image restoration. Digital Image Inpainting is a technique which addresses such an issue. Inpainting techniques are based on interpolation, diffusion or exemplar based concepts. This paper briefly describes the application of such concepts for inpainting and pr...