Joint image fusion and denoising via three-layer decomposition and sparse representation

Image fusion has been received much attentions in recent years. However, solving both noise-free image fusion and noise-perturbed image fusion problems remains a big challenge. To solve the weak performance and low computational efficiency for current image fusion methods when dealing with the case of noisy source images, an image fusion method based on three-layer decomposition and sparse representation is proposed in this paper. In view of the high-pass characteristics of noise, the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level, so as to realize the fusion and denoising for high-frequency components simultaneously. To make full use of the details and energy in the low-frequency component, the structure-texture decomposition model is carried out and two fusion rules are carefully designed to fuse them. The fused image can be reconstructed by the perfused high-frequency, low-frequency structure and low-frequency texture layers. Experimental results demonstrate that the proposed method can effectively address the clean and noisy image fusion problems, and yield better performance than some state-of-the-art methods in terms of subjective visual and quantitative evaluations. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The proposed image fusion method is based on three-layer decomposition and sparse representation, which effectively fuses and denoises high-frequency components by adaptively designing sparse reconstruct error parameters. Additionally, the structure-texture decomposition model and carefully designed fusion rules are used to fully utilize details and energy in low-frequency components. The experimental results show that this method can effectively address clean and noisy image fusion problems and outperform some state-of-the-art methods in subjective visual and quantitative evaluations.