Learning Multi-Layer Transform Models

TL;DR

This paper introduces multi-layer transform models for sparse data representation, proposing efficient learning algorithms and demonstrating improved image denoising performance over single-layer models.

Contribution

It presents a novel multi-layer transform learning framework with efficient algorithms, enhancing sparsity-based image denoising methods.

Findings

Multi-layer models outperform single-layer schemes in denoising quality.

The proposed algorithms are effective and computationally efficient.

Numerical experiments validate the advantages of multi-layer transform models.

Abstract

Learned data models based on sparsity are widely used in signal processing and imaging applications. A variety of methods for learning synthesis dictionaries, sparsifying transforms, etc., have been proposed in recent years, often imposing useful structures or properties on the models. In this work, we focus on sparsifying transform learning, which enjoys a number of advantages. We consider multi-layer or nested extensions of the transform model, and propose efficient learning algorithms. Numerical experiments with image data illustrate the behavior of the multi-layer transform learning algorithm and its usefulness for image denoising. Multi-layer models provide better denoising quality than single layer schemes.