DeshadowMamba: Deshadowing as 1D Sequential Similarity

TL;DR

DeshadowMamba introduces a novel sequence modeling approach for shadow removal, utilizing a selective state space model with shadow-aware modulation and color regularization to improve structural and color fidelity.

Contribution

The paper presents DeshadowMamba, a new shadow removal method that combines a directional state space model with shadow-aware modulation and contrastive color regularization.

Findings

Achieves state-of-the-art results on public benchmarks.

Improves structural integrity and color consistency in shadow removal.

Demonstrates robustness across diverse shadow scenarios.

Abstract

Recent deep models for image shadow removal often rely on attention-based architectures to capture long-range dependencies. However, their fixed attention patterns tend to mix illumination cues from irrelevant regions, leading to distorted structures and inconsistent colors. In this work, we revisit shadow removal from a sequence modeling perspective and explore the use of Mamba, a selective state space model that propagates global context through directional state transitions. These transitions yield an efficient global receptive field while preserving positional continuity. Despite its potential, directly applying Mamba to image data is suboptimal, since it lacks awareness of shadow-non-shadow semantics and remains susceptible to color interference from nearby regions. To address these limitations, we propose CrossGate, a directional modulation mechanism that injects shadow-aware similarity into Mamba's input gate, allowing selective integration of relevant context along transition axes. To further ensure appearance fidelity, we introduce ColorShift regularization, a contrastive learning objective driven by global color statistics. By synthesizing structured informative negatives, it guides the model to suppress color contamination and achieve robust color restoration. Together, these components adapt sequence modeling to the structural integrity and chromatic consistency required for shadow removal. Extensive experiments on public benchmarks demonstrate that DeshadowMamba achieves state-of-the-art visual quality and strong quantitative performance.