Analysis of Inpainting via Clustered Sparsity and Microlocal Analysis

TL;DR

This paper provides a comprehensive mathematical analysis of image inpainting techniques using clustered sparsity and microlocal analysis, demonstrating the superior performance of shearlets over wavelets in recovering images with missing data.

Contribution

It introduces a novel analytical framework combining clustered sparsity and microlocal analysis to evaluate inpainting methods, highlighting the advantages of directional systems like shearlets.

Findings

Shearlets outperform wavelets in filling larger gaps in images.

Asymptotic error bounds are derived for l1 minimization and thresholding.

Directional representations provide better geometrical content recovery.

Abstract

Recently, compressed sensing techniques in combination with both wavelet and directional representation systems have been very effectively applied to the problem of image inpainting. However, a mathematical analysis of these techniques which reveals the underlying geometrical content is completely missing. In this paper, we provide the first comprehensive analysis in the continuum domain utilizing the novel concept of clustered sparsity, which besides leading to asymptotic error bounds also makes the superior behavior of directional representation systems over wavelets precise. First, we propose an abstract model for problems of data recovery and derive error bounds for two different recovery schemes, namely l_1 minimization and thresholding. Second, we set up a particular microlocal model for an image governed by edges inspired by seismic data as well as a particular mask to model the missing data, namely a linear singularity masked by a horizontal strip. Applying the abstract estimate in the case of wavelets and of shearlets we prove that -- provided the size of the missing part is asymptotically to the size of the analyzing functions -- asymptotically precise inpainting can be obtained for this model. Finally, we show that shearlets can fill strictly larger gaps than wavelets in this model.