标题： AFUNet：通过深度展开范式进行HDR重建的交叉迭代对齐-融合协同 显示英文标题

标题： AFUNet: Cross-Iterative Alignment-Fusion Synergy for HDR Reconstruction via Deep Unfolding Paradigm

摘要： 现有基于学习的方法能够从多曝光的低动态范围输入中有效重建高动态范围图像，扩展动态范围并提高细节，但它们更多依赖于经验设计而非理论基础，这可能会影响其可靠性。 为解决这些限制，我们提出了交叉迭代对齐与融合深度展开网络（AFUNet），其中高动态范围重建被系统地解耦为两个交错的子任务——对齐和融合——通过交替优化实现，从而在两个子任务之间产生协同作用，以提高整体性能。 我们的方法从最大后验（MAP）估计的角度对多曝光高动态范围重建进行建模，显式地结合了低动态范围图像之间的空间对应先验，并通过联合约束自然地连接对齐和融合子问题。 基于数学基础，我们重新设想了传统的迭代优化过程——将传统求解过程转化为一个端到端可训练的AFUNet，其中包含精心设计的模块，逐步工作。 具体而言，AFUNet的每次迭代都包含一个对齐-融合模块（AFM），该模块在用于对齐的空间对齐模块（SAM）和用于自适应特征融合的通道融合模块（CFM）之间交替进行，逐步弥合不对齐内容和曝光差异。 大量的定性和定量评估证明了AFUNet的优越性能，始终超越最先进的方法。 我们的代码可在以下网址获取：https://github.com/eezkni/AFUNet 显示英文摘要

摘要： Existing learning-based methods effectively reconstruct HDR images from multi-exposure LDR inputs with extended dynamic range and improved detail, but they rely more on empirical design rather than theoretical foundation, which can impact their reliability. To address these limitations, we propose the cross-iterative Alignment and Fusion deep Unfolding Network (AFUNet), where HDR reconstruction is systematically decoupled into two interleaved subtasks -- alignment and fusion -- optimized through alternating refinement, achieving synergy between the two subtasks to enhance the overall performance. Our method formulates multi-exposure HDR reconstruction from a Maximum A Posteriori (MAP) estimation perspective, explicitly incorporating spatial correspondence priors across LDR images and naturally bridging the alignment and fusion subproblems through joint constraints. Building on the mathematical foundation, we reimagine traditional iterative optimization through unfolding -- transforming the conventional solution process into an end-to-end trainable AFUNet with carefully designed modules that work progressively. Specifically, each iteration of AFUNet incorporates an Alignment-Fusion Module (AFM) that alternates between a Spatial Alignment Module (SAM) for alignment and a Channel Fusion Module (CFM) for adaptive feature fusion, progressively bridging misaligned content and exposure discrepancies. Extensive qualitative and quantitative evaluations demonstrate AFUNet's superior performance, consistently surpassing state-of-the-art methods. Our code is available at: https://github.com/eezkni/AFUNet