Shadow removal aims to restore photometric, chromatic, and structural consistency between shadowed and non-shadowed image regions. Although weakly supervised shadow removal methods reduce the reliance on densely paired training data, they still struggle to fully exploit appearance priors from non-shadow regions. As a result, their shadow removal outputs often appear unnatural, exhibiting color shifts and loss of fine texture details. To address this issue, we propose an ab-dynamic feature refinement network (AB-DFRNet) for weakly supervised shadow removal that more effectively exploits structural and chromatic symmetry during training. A high-frequency information enhancement (HFIE) module is introduced into the shadow generation subnet to extract and enhance high-frequency components via frequency separation and dense convolutions, thereby facilitating the learning of fine structural symmetry and enriching pseudo-shadow details. In the removal subnet, a dual-attention adaptive fusion (DAAF) module combines global and local attention mechanisms to adaptively recalibrate channel-wise and spatial features, improving multi-scale feature integration. Furthermore, a chrominance-only consistency (COC) loss is designed to minimize differences between the a and b channels of restored regions and their non-shadow references in the Lab color space. This additional color refinement constraint encourages a symmetric distribution of chromatic information and helps the refinement network produce more natural shadow-removed results. Extensive experiments are conducted on three benchmark datasets: ISTD, SRD, and Video Shadow Removal. The results confirm the effectiveness of AB-DFRNet, demonstrating competitive quantitative performance and noticeably better visual quality compared with existing weakly supervised shadow removal methods.
Shao et al. (Wed,) studied this question.