Global hard thresholding algorithms for joint sparse image representation and denoising
This addresses a domain-specific issue in image processing by improving sparse coding efficiency for denoising, but it is incremental as it builds on existing patch-based methods.
The paper tackles the problem of inefficient distribution of nonzero coefficients across image patches in sparse coding by proposing a joint sparse representation framework and two global hard thresholding algorithms, showing effectiveness in sparse image representation and denoising tasks with high scalability for large images.
Sparse coding of images is traditionally done by cutting them into small patches and representing each patch individually over some dictionary given a pre-determined number of nonzero coefficients to use for each patch. In lack of a way to effectively distribute a total number (or global budget) of nonzero coefficients across all patches, current sparse recovery algorithms distribute the global budget equally across all patches despite the wide range of differences in structural complexity among them. In this work we propose a new framework for joint sparse representation and recovery of all image patches simultaneously. We also present two novel global hard thresholding algorithms, based on the notion of variable splitting, for solving the joint sparse model. Experimentation using both synthetic and real data shows effectiveness of the proposed framework for sparse image representation and denoising tasks. Additionally, time complexity analysis of the proposed algorithms indicate high scalability of both algorithms, making them favorable to use on large megapixel images.