用于多聚焦图像融合的清晰度对比与相似性选择
Clarity Contrast and Similarity Selection for Multi-Focus Image Fusion
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中文总结 AI 辅助
该研究针对多聚焦图像融合中源图像交互不足的问题，提出CSNet模型，通过CCAM和相似性选择策略实现信息交互，在定量和定性评估中达到当前最优性能。
中文摘要 AI 辅助
多聚焦图像融合（MFIF）旨在从同一场景、不同区域聚焦的多张图像中生成一张全聚焦图像。现有多数基于深度学习的方法缺乏源图像间的显式交互，这限制了其性能与可解释性。本文提出一种新颖的清晰度对比与相似性选择网络（CSNet），为MFIF搭建直接信息交换的桥梁。具体而言，通过在我们提出的清晰度对比注意力模块（CCAM）中对比源图像间的清晰度差异，我们相互增强清晰特征同时抑制模糊特征，这使我们能识别每张源图像中精确聚焦的区域并定位聚焦-散焦边界。此外，散焦扩散效应（DSE）会降低边界周围所有源图像中像素的质量。为进一步优化这些模糊区域，我们引入相似性选择策略，该策略从源图像中重构一张初始清晰图像，并通过比较源图像间的相似性选择最优像素。通过这种交互方法，CSNet能有效保留聚焦区域并恢复自然边界，从而生成全聚焦输出图像。大量实验表明，我们的方法在定量和定性评估上均达到了当前最优性能，代码可在Github获取：this https URL。
英文摘要
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.
