In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information of where the image is corrupted, and others that do it. While the first algorithm is able to reconstruct only images that our visual system is...

PURPOSE The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is present...

Reconstruction of missing or damaged portions of images is an ancient practice used extensively in artwork restoration. Recently, a few digital inpainting models based on the use of partial differential equations have been proposed. Unfortunately, these algorithms are computationally expensive, usually taking a few minutes to restore small portions of an image, which makes them inappropriate fo...

Image inpainting also referred to as image restoration or completion is a technique used to fill-in the missing region or damaged region of an image by using the known information of an image. There are different approaches for image inpainting. First approach is diffusion based approach in which the target or missing region is filled in by diffusing the image information from the source region...

We are often required to retouch images in order to improve their visual appearance, by removing the visual discontinuities like breaks and damaged regions. Such retouching of images may be achieved by inpainting. Current techniques for image inpainting require the user to manually select the target regions to be inpainted. Very few techniques for automatically detecting the target regions for ...

To restore the underexposure image, an illumination compensation inpainting model which employs the joint-diffused partial differential equations (PDEs) is proposed. Firstly, the novel model compensates the illumination effect in multi-scaled underexposure images respectively. Secondly, the information in the fused compensated image is restored by PDEs which diffuse the geometric property and g...

Abstract The image inpainting is an important problem image processing. It is a difficult problem to recover an image with bigger hole, and the existing algorithms are not give good method. Based on fractal theory, in this paper, we present a new inpainting method, which using the total info of image effectively. The relation of fractal dimension and the size range block is discussed, and give ...

Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions. However, these models cannot be directly applied on image/video sequences because of an intrinsic drawbackthe reconstructions might be independently realistic, but, when visualized as a sequence, often lacks fidelity to the or...

Recently, there has been a strong ambition to translate models and algorithms from traditional image processing to non-Euclidean domains, e.g., to manifold-valued data. While the task of denoising has been extensively studied in the last years, there was rarely an attempt to perform image inpainting on manifold-valued data. In this paper we present a nonlocal inpainting method for manifold-valu...

Inpainting is the technique of reconstructing unknown or damaged portions of an image in a visually plausible way. Inpainting algorithm automatically fills the damaged region in an image using the information available in undamaged region. Propagation of structure and texture information becomes a challenge as the size of damaged area increases. In this paper, a hierarchical inpainting algorith...