AmpFormer: Amplitude-Aware Spectral Recalibration for Shadow Removal

Recent years have witnessed significant progress in deep learning-based shadow removal. However, most prior methods operate primarily in the spatial domain or rely on coarse frequency cues, while the informative role of amplitude components in the frequency domain remains largely unexplored. The amplitude spectrum encodes spectral energy that reflects global illumination and fine texture that strongly influence shadow appearance. Motivated by this observation, we propose AmpFormer, a U-shaped transformer architecture that explicitly models amplitude information for robust shadow correction. Central to AmpFormer is a lightweight SFR module inserted at each encoder–decoder stage: SFR extracts multi-scale amplitude cues from compact spectral representations, learns per-channel adaptive gains and subtle phase adjustments, and injects the recalibrated frequency features into the spatial stream. To further encourage amplitude-aware restoration, we introduce an amplitude loss that explicitly regularizes spectral energy with emphasis on global illumination consistency. Extensive experiments on standard benchmarks demonstrate that AmpFormer achieves state-of-the-art restoration quality while offering a favorable computational-efficiency-accuracy trade-off, validating the practical benefit of amplitude-aware frequency modeling for shadow removal.