Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. Many researchers have proposed a large variety of exemplar based image inpainting algorithms to restore the structure and texture of damaged images. However no recent study has been undertaken for a comparative evaluation of these algorithms. In this paper, we are comparing va...

Video inpainting consists in recovering the missing or corrupted parts of an image sequence so that the reconstructed sequence looks natural. For each frame, the reconstruction has to be spatially coherent with the rest of the image and temporally with respect to the reconstructions of adjacent frames. Most of existing methods only focus on inpainting foreground objects moving with a periodic m...

Due to the changes in viewpoint, holes may appear in the novel view synthesized by depth-image-based rendering (DIBR). A hole-filling method combining disparity-mapbased hole-filling and inpainting is proposed. The method first eliminates matching errors in destination image according to the disparity map. Then, holes are classified into different types according to their sizes. Finally, a disp...

In this paper, we analyze if cascade usage of the context encoder with increasing input can improve the results of the inpainting. For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase. As the result, the inpainting is visibly more p...

This paper focuses a method for cloud detection and their reconstruction technique. Detecting these portions of an image and then filling in the missing data is an important photo editing work. The filling-in approach such as inpainting techniques, which aim at filling holes in remote sensing images by propagating surrounding structures and texture information. Inpainting technique is a novel m...

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...

We introduce a fourth-order total variation flow for image inpainting proposed in [5]. The well-posedness of this new inpainting model is discussed and its efficient numerical realization via an unconditionally stable solver developed in [15] is presented.

We address the problem of surface inpainting, which aims to fill in holes or missing regions on a Riemann surface based on its surface geometry. In practical situation, surfaces obtained from range scanners often have holes or missing regions where the 3D models are incomplete. In order to analyze the 3D shapes effectively, restoring the incomplete shape by filling in the surface holes is neces...

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers [15]. In this work, we propose a novel head inpainting ...

Image inpainting is a technique to recover lost data or manually remove data from an image. This process is carried out in such a way that it seems reasonable to human eyes. The lost part of the image is filled with the remaining available data of the image. Inpainting can be done using various techniques, mainly variation based, exemplar based and wavelet based. In this paper, exemplar based i...