Contrast-Prior Enhanced Duality for Mask-Free Shadow Removal

TL;DR

This paper introduces a novel mask-free shadow removal method using contrast priors, adaptive attention, and diffusion-based frequency-contrast fusion, achieving state-of-the-art results without relying on shadow masks.

Contribution

It proposes the AGBA mechanism for effective contrast prior filtering and a diffusion-based FCFN for detailed shadow boundary restoration, advancing mask-free shadow removal techniques.

Findings

Achieves state-of-the-art results among mask-free methods.

Maintains competitive performance with mask-based approaches.

Effectively disentangles shadow features from complex backgrounds.

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.