Modulo imaging enables high dynamic range (HDR) acquisition by cyclically wrapping saturated intensities, but accurate reconstruction remains challenging due to ambiguities between natural image edges and artificial wrap discontinuities. This work proposes a learning-based HDR restoration framework that incorporates two key strategies: (i) a scale-equivariant regularization that enforces consistency under exposure variations, and (ii) a feature lifting input design combining the raw modulo image, wrapped finite differences, and a closed-form initialization. Together, these components enhance the network's ability to distinguish true structure from wrapping artifacts, yielding state-of-the-art performance across perceptual and linear HDR quality metrics.

翻译：模成像通过循环包裹饱和强度实现高动态范围采集，但由于自然图像边缘与人为包裹间断之间的模糊性，精确重建仍具挑战。本研究提出一种基于学习的高动态范围重建框架，包含两项关键策略：(i) 通过尺度等变正则化强制曝光变化下的重建一致性；(ii) 采用融合原始模图像、包裹有限差分及闭式初始化的特征提升输入设计。这些组件共同增强了网络区分真实结构与包裹伪影的能力，在感知性与线性高动态范围质量指标上均实现了最先进的性能。