Simultaneous Sparse Dictionary Learning and Pruning

TL;DR

This paper introduces a novel regularization method, GSCAD, for simultaneous sparse dictionary learning and pruning, improving efficiency and accuracy in image processing tasks.

Contribution

It proposes a new regularization technique, GSCAD, combined with ADMM to learn a sparse dictionary and determine its optimal size simultaneously.

Findings

Effective in image denoising tasks

Outperforms existing dictionary learning methods

Automatically selects appropriate dictionary size

Abstract

Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining a proper size of the to-be-learned dictionary is crucial for both precision and efficiency of the process, while most of the existing dictionary learning algorithms choose the size quite arbitrarily. In this paper, a novel regularization method called the Grouped Smoothly Clipped Absolute Deviation (GSCAD) is employed for learning the dictionary. The proposed method can simultaneously learn a sparse dictionary and select the appropriate dictionary size. Efficient algorithm is designed based on the alternative direction method of multipliers (ADMM) which decomposes the joint non-convex problem with the non-convex penalty into two convex optimization problems. Several examples are presented for image denoising and the experimental results are compared with other state-of-the-art approaches.