Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution

TL;DR

This paper investigates how different sparse recovery algorithms impact the quality of image superresolution when using dictionary learning, through empirical experiments to identify the most effective method.

Contribution

It provides an empirical analysis of various sparse recovery algorithms to determine their influence on superresolution image quality.

Findings

Certain sparse recovery algorithms significantly improve image quality.

Optimal choice of sparse recovery method enhances superresolution results.

Empirical results guide better algorithm selection for image reconstruction.

Abstract

Dictionary learning can be used for image superresolution by learning a pair of coupled dictionaries of image patches from high-resolution and low-resolution image pairs such that the corresponding pairs share the same sparse vector when represented by the coupled dictionaries. These dictionaries then can be used to to reconstruct the corresponding high-resolution patches from low-resolution input images based on sparse recovery. The idea is to recover the shared sparse vector using the low-resolution dictionary and then multiply it by the high-resolution dictionary to recover the corresponding high-resolution image patch. In this work, we study the effect of the sparse recovery algorithm that we use on the quality of the reconstructed images. We offer empirical experiments to search for the best sparse recovery algorithm that can be used for this purpose.