A Variational Framework For Non Local Image Inpainting
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A Variational Framework for Non-Local Image Inpainting
Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images and regions, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for the problem of non-local image inpainting, from which several previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.
Energy Minimization Methods in Computer Vision and Pattern Recognition
This book constitutes the refereed proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2009, held in Bonn, Germany in August 2009. The 18 revised full papers, 18 poster papers and 3 keynote lectures presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on discrete optimization and Markov random fields, partial differential equations, segmentation and tracking, shape optimization and registration, inpainting and image denoising, color and texture and statistics and learning.
Partial Differential Equation Methods for Image Inpainting
Author: Carola-Bibiane Schönlieb
language: en
Publisher: Cambridge University Press
Release Date: 2015-10-26
This book introduces the mathematical concept of partial differential equations (PDE) for virtual image restoration. It provides insight in mathematical modelling, partial differential equations, functional analysis, variational calculus, optimisation and numerical analysis. It is addressed towards generally informed mathematicians and graduate students in mathematics with an interest in image processing and mathematical analysis.