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This paper introduces the Clarity Contrast and Similarity Selection Network (CSNet) for multi-focus image fusion (MFIF), addressing the limitations of existing deep learning methods that lack direct interaction between source images. By employing a Clarity Contrast Attention Module (CCAM) to enhance sharp features while suppressing blurriness, CSNet effectively identifies focused regions and refines ambiguous boundaries using a Similarity Selection Strategy. Extensive experiments show that CSNet outperforms state-of-the-art methods in both quantitative and qualitative assessments, highlighting its potential for generating superior all-in-focus images.
CSNet achieves state-of-the-art image fusion by intelligently contrasting clarity and selecting optimal pixels, redefining how we approach multi-focus image challenges.
Multi-focus image fusion (MFIF) aims to generate an all-in-focus image from multiple images of the same scene focused at different regions. Most existing deep learning-based methods lack explicit interaction between the source images, which limits their performance and interpretability. This paper presents a novel Clarity Contrast and Similarity Selection Network (CSNet), to bridge direct information exchange for MFIF. Specifically, by contrasting the clarity differences between source images within our proposed Clarity Contrast Attention Module (CCAM), we mutually enhance sharp features while suppressing blurry ones. This allows us to identify the exactly focused regions in each source and locate the focused-defocused boundaries. Moreover, the Defocus Spread Effect (DSE) degrades pixels in all source images around the boundaries. To further refine these ambiguous areas, we introduce a Similarity Selection Strategy, which reconstructs an initial clear image from source images and selects optimal pixels by comparing the similarity among them. Through this interactive approach, CSNet effectively preserves focused regions as well as recovering natural boundaries to fuse an all-in-focus output. Extensive experiments demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively. Our code is available on Github: https://github.com/ZYC-HUST/CSNet.
