Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection

TL;DR

This paper introduces an adaptive block size selection method for local sparse approximation in image restoration, improving denoising and inpainting by minimizing mean square error and clustering image regions.

Contribution

It proposes a novel adaptive block size selection procedure that enhances local sparse approximation for image restoration tasks.

Findings

Results are comparable with recent state-of-the-art techniques.

The method effectively clusters image regions based on block size.

Adaptive selection improves restoration quality in denoising and inpainting.

Abstract

In this paper the problem of image restoration (denoising and inpainting) is approached using sparse approximation of local image blocks. The local image blocks are extracted by sliding square windows over the image. An adaptive block size selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive local block selection yields the minimum mean square error (MMSE) in recovered image. This framework gives us a clustered image based on the selected block size, then each cluster is restored separately using sparse approximation. The results obtained using the proposed framework are very much comparable with the recently proposed image restoration techniques.