Consistency as Regularization for Unsupervised Shadow Removal

2026-09-01

AI Summary

本文提出ShadowCLR，一种无监督框架，通过利用阴影图像间的一致性作为正则化手段，直接从阴影图像中学习去除阴影，无需阴影掩码或无阴影参考图像。

Abstract

Shadow removal is an important preprocessing step for many vision tasks, yet existing supervised methods require paired shadow and shadow-free images, while unsupervised approaches often still rely on shadow masks or shadow-free references. We propose ShadowCLR, an unsupervised framework that learns shadow removal directly from shadow images. Our key observation is that shadows vary across observations while the underlying scene content remains largely consistent. We therefore use consistency across shadow observations as regularization, encouraging the model to recover scene-consistent appearance while suppressing shadow-specific variations. Global and local consistency further enable us to explore visually related images, learn from imperfectly aligned observations, and focus the representation on shared scene information. Experiments on multiple benchmarks show that ShadowCLR achieves competitive and often superior performance over state-of-the-art unsupervised methods, demonstrating that consistency can provide regularization for shadow removal without shadow masks or shadow-free images.

Problem

• unsupervised shadow removal
• shadow images
• consistency regularization

Innovation

• Unsupervised Learning
• Consistency as Regularization
• Shadow Removal
• Scene Consistency
• Visual Alignment