[IPOL announce] new article: Non-Local Patch-Based Image Inpainting

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2017/189/

Alasdair Newson, Andrés Almansa, Yann Gousseau, and Patrick Pérez,
Non-Local Patch-Based Image Inpainting,
Image Processing On Line, 7 (2017), pp. 373–385.
https://doi.org/10.5201/ipol.2017.189

Abstract
Image inpainting is the process of filling in missing regions in an 
image in a plausible way. In this contribution, we propose and describe 
an implementation of a patch-based image inpainting algorithm. The 
method is actually a two-dimensional version of our video inpainting 
algorithm proposed in [A. Newson et al., Video inpainting of complex 
scenes, SIAM Journal of Imaging Sciences, 7 (2014)]. The algorithm 
attempts to minimize a highly non-convex functional, first introducted 
by Wexler et al. in [Wexler et al., Space-time video completion, CCVPR 
(2004)]. The functional specifies that a good solution to the inpainting 
problem should be an image where each patch is very similar to its 
nearest neighbor in the unoccluded area. Iterations are performed in a 
multi-scale framework which yields globally coherent results. In this 
manner two of the major goals of image inpainting, the correct 
reconstruction of textures and structures, are addressed. We address a 
series of important practical issues which arise when using such an 
approach. In particular, we reduce execution times by using the 
PatchMatch [C. Barnes, PatchMatch: a randomized correspondence algorithm 
for structural image editing, ACM Transactions on Graphics, (2009)] 
algorithm for nearest neighbor searches, and we propose a modified patch 
distance which improves the comparison of textured patches. We address 
the crucial issue of initialization and the choice of the number of 
pyramid levels, two points which are rarely discussed in such 
approaches. We provide several examples which illustrate the advantages 
of our algorithm, and compare our results with those of state-of-the-art 
methods.