Simultaneous image fusion and denoising with adaptive sparse representation

TLDR

Sparse representation has been widely used for image denoising and fusion, but conventional methods rely on highly redundant dictionaries that can introduce visual artifacts and incur high computational cost. This study introduces an adaptive sparse representation (ASR) model designed to perform image fusion and denoising simultaneously. The ASR framework learns a set of compact sub‑dictionaries from high‑quality image patches pre‑classified by gradient information, and then adaptively selects the appropriate sub‑dictionary for each set of source patches during fusion and denoising. Experiments on multi‑focus and multi‑modal image sets show that the ASR‑based method surpasses traditional SR‑based approaches in both visual quality and objective metrics.

Abstract

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR‐based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub‐dictionaries are learned from numerous high‐quality image patches which have been pre‐classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub‐dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi‐focus and multi‐modal image sets demonstrate that the ASR‐based fusion method can outperform the conventional SR‐based method in terms of both visual quality and objective assessment.