In spite of numerous works on inpainting, there has been little work addressing both image and structure inpainting. In this work, we propose a new method for inpainting both image and depth of a scene using multiple stereo images. The observations contain unwanted artifacts, which can be possibly caused due to sensor/lens damage or occluders. In such a case, all the observations contain missin...

Image inpainting technique is that repairs damaged area or remove areas in an image. In order to deal with this kind of problems, not only a robust image inpainting algorithm should be used, technique of structure generation is also needs to adopt in inpainting procedure. In this paper, we extend an exemplar-based image inpainting method by incorporating Bézier curves to construct missing edge ...

We propose a framework for temporally consistent video completion. To this end we generalize the exemplar-based inpainting method of Criminisi et al . [7] to video inpainting. Specifically we address two important issues: Firstly, we propose a color and optical flow inpainting to ensure temporal consistency of inpainting even for complex motion of foreground and background. Secondly, rather tha...

Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resolution images with neural networks is still an open...

Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. There are a large variety of image inpainting algorithms existing in the literature. They can broadly be grouped into two categories such as Partial Differential Equation (PDE) based algorithms and Exemplar based Texture synthesis algorithms. However no recent study has been u...

Image inpainting refers to the set of techniques which include filling-in of missing area (known as holes) in a picture such that modifications made to the picture are unnoticeable. The algorithms for image inpainting that are proposed in the literature are rooted on the concept to fill-in the holes by means of available information in the surroundings. This information can be automatically det...

The filling-in effect of diffusion processes has been successfully used in many image analysis applications. Examples include image reconstructions in inpainting-based compression or dense optic flow computations. As an interesting side effect of diffusion-based inpainting, the interpolated data are smooth, even if the known image data are noisy: Inpainting averages information from noisy sourc...

This paper present a combined method for image inpainting. Inpainting is the art of modifying an image in a visually plausible manner so that it seems reasonable to the human eye. There are numerous and very different approaches to tackle the inpainting problem, these are based upon one the following basic techniques: copy-and-paste texture synthesis, geometric partial differential equations (P...

We discuss an extension of our method image inpainting based on coherence transport. For the latter method the pixels of the inpainting domain have to be serialized into an ordered list. Until now, to induce the serialization we have used the distance to boundary map. But there are inpainting problems where the distance to boundary serialization causes unsatisfactory inpainting results. In the ...

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 ...