期刊
出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219691320500617
关键词
Multifocus image fusion; convolutional sparse representation; multiscale transform; contrast enhancement; detail preservation
资金
- Sichuan Science and Technology Program [2020YFS0351]
- Luzhou Science and Technology Program [2019-SYF-34]
- Scientific Research Project of Sichuan Public Security Department [201917]
In this paper, a fusion method based on MST and CSR is proposed to address the inherent defects of traditional fusion methods. Experimental results show that the algorithm exhibits state-of-the-art performance in terms of definition.
Multifocus image fusion can obtain an image with all objects in focus, which is beneficial for understanding the target scene. Multiscale transform (MST) and sparse representation (SR) have been widely used in multifocus image fusion. However, the contrast of the fused image is lost after multiscale reconstruction, and fine details tend to be smoothed for SR-based fusion. In this paper, we propose a fusion method based on MST and convolutional sparse representation (CSR) to address the inherent defects of both the MST- and SR-based fusion methods. MST is first performed on each source image to obtain the low-frequency components and detailed directional components. Then, CSR is applied in the low-pass fusion, while the high-pass bands are fused using the popular max-absolute rule as the activity level measurement. The fused image is finally obtained by performing inverse MST on the fused coefficients. The experimental results on multifocus images show that the proposed algorithm exhibits state-of-the-art performance in terms of definition.
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