Multimodal Image Denoising based on Coupled Dictionary Learning
This addresses image denoising for applications using multimodal data, but it is incremental as it builds on existing dictionary learning techniques.
The paper tackles multimodal image denoising by using coupled dictionary learning to attenuate white Gaussian additive noise with guidance from another image modality, resulting in notable benefits over state-of-the-art methods by reducing texture copying artifacts.
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.