Contrast-Prior Enhanced Duality for Mask-Free Shadow Removal

• URL: http://arxiv.org/abs/2507.21949v1
• Date: Tue, 29 Jul 2025 16:00:42 GMT
• Title: Contrast-Prior Enhanced Duality for Mask-Free Shadow Removal
• Authors: Jiyu Wu, Yifan Liu, Jiancheng Huang, Mingfu Yan, Shifeng Chen,
• Abstract summary: Existing shadow removal methods often rely on shadow masks, which are challenging to acquire in real-world scenarios.<n> Exploring intrinsic image cues, such as local contrast information, presents a potential alternative for guiding shadow removal in the absence of explicit masks.<n>We propose the Adaptive Gated Dual-Branch Attention (AGBA) mechanism, which filters and re-weighs the contrast prior to effectively disentangle shadow features.
• Score: 12.417806583744134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing shadow removal methods often rely on shadow masks, which are challenging to acquire in real-world scenarios. Exploring intrinsic image cues, such as local contrast information, presents a potential alternative for guiding shadow removal in the absence of explicit masks. However, the cue's inherent ambiguity becomes a critical limitation in complex scenes, where it can fail to distinguish true shadows from low-reflectance objects and intricate background textures. To address this motivation, we propose the Adaptive Gated Dual-Branch Attention (AGBA) mechanism. AGBA dynamically filters and re-weighs the contrast prior to effectively disentangle shadow features from confounding visual elements. Furthermore, to tackle the persistent challenge of restoring soft shadow boundaries and fine-grained details, we introduce a diffusion-based Frequency-Contrast Fusion Network (FCFN) that leverages high-frequency and contrast cues to guide the generative process. Extensive experiments demonstrate that our method achieves state-of-the-art results among mask-free approaches while maintaining competitive performance relative to mask-based methods.

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