用四元数域方法融合多焦点彩色图像，提升细节与结构保真度。
Quaternion Sparse Decomposition for Multi-focus Color Image Fusion
- 在四元数域内联合学习颜色图像的细节与结构信息。
- 通过基底-细节分离策略有效保留纹理与结构特征。
- 自适应选择最优块，确保输出空间一致性与精细细节保留。
多焦点彩色图像融合旨在将多张部分聚焦的彩色图像合成一张全清晰图像。现有方法在复杂真实场景中受限于对颜色信息和复杂纹理的处理能力。本文提出一种完全在四元数域进行的多焦点彩色图像融合框架，包含：1）四元数稀疏分解模型，通过迭代方式联合学习图像的细粒度细节与结构信息，实现高精度聚焦检测；2）四元数基底-细节融合策略，分别融合多幅图像的基底尺度与细节尺度结果，以保留结构与细节；3）四元数结构相似性优化策略，自适应选取初始融合结果中的最优局部区域，生成最终融合图像，有效保持精细细节并确保空间一致性。大量实验表明，该框架优于当前最先进的方法。
原文摘要 · Abstract (English)
Multi-focus color image fusion refers to integrating multiple partially focused color images to create a single all-in-focus color image. However, existing methods struggle with complex real-world scenarios due to limitations in handling color information and intricate textures. To address these challenges, this paper proposes a quaternion multi-focus color image fusion framework to perform high-quality color image fusion completely in the quaternion domain. This framework introduces 1) a quaternion sparse decomposition model to jointly learn fine-scale image details and structure information of color images in an iterative fashion for high-precision focus detection, 2) a quaternion base-detail fusion strategy to individually fuse base-scale and detail-scale results across multiple color images for preserving structure and detail information, and 3) a quaternion structural similarity refinement strategy to adaptively select optimal patches from initial fusion results and obtain the final fused result for preserving fine details and ensuring spatially consistent outputs. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods.
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