Multimodal Image Denoising based on Coupled Dictionary Learning

TL;DR

This paper introduces a novel multimodal image denoising method that leverages coupled dictionary learning to effectively reduce noise by utilizing guidance images, capturing shared and unique features for improved robustness.

Contribution

It presents a new coupled sparse coding and reconstruction framework that enhances denoising performance by better exploiting multimodal guidance information.

Findings

Outperforms state-of-the-art methods on real multimodal images

Effectively captures common and distinct features of modalities

Reduces texture copying artifacts in denoised images

Abstract

In this paper, we propose a new multimodal image denoising approach to attenuate white Gaussian additive noise in a given image modality under the aid of a guidance image modality. The proposed coupled image denoising approach consists of two stages: coupled sparse coding and reconstruction. The first stage performs joint sparse transform for multimodal images with respect to a group of learned coupled dictionaries, followed by a shrinkage operation on the sparse representations. Then, in the second stage, the shrunken representations, together with coupled dictionaries, contribute to the reconstruction of the denoised image via an inverse transform. The proposed denoising scheme demonstrates the capability to capture both the common and distinct features of different data modalities. This capability makes our approach more robust to inconsistencies between the guidance and the target images, thereby overcoming drawbacks such as the texture copying artifacts. Experiments on real multimodal images demonstrate that the proposed approach is able to better employ guidance information to bring notable benefits in the image denoising task with respect to the state-of-the-art.