ODDR: One-Step Deshadow Diffusion via Reward Guidance

2026-10-01

AI Summary

This work addresses the limited generalization of shadow removal methods caused by their reliance on costly real paired data by proposing the ODDR framework. Specifically, it trains a one-step shadow removal diffusion model exclusively on synthetic data. To bridge the domain gap between synthetic and real images, this study introduces ShadowReward, a novel reward model that eliminates the need for manual annotations by ranking synthetically degraded images to simulate human perception, thereby guiding reinforcement learning fine-tuning. By effectively aligning synthetic training with real-world distributions, ODDR achieves shadow removal performance comparable to fully supervised approaches while preserving single-step inference efficiency. Consequently, this method significantly improves image restoration quality in scenarios where paired real data is unavailable.

Abstract

Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world paired datasets, which are costly to collect and often limited in scene diversity, leading to limited generalization. To address these limitations, we propose One-step Deshadow Diffusion via Reward guidance (ODDR), a new framework that achieves efficient and high-fidelity shadow removal without relying on real-world paired supervision. Our method begins with One-step Deshadow Diffusion (ODD), a baseline model trained on synthetic shadow data for efficient one-step shadow-free reconstruction. We further adapt ODD into ODDR using ShadowReward. In contrast to traditional, annotation-heavy approaches, ShadowReward is the first reward model for shadow removal trained entirely without human annotation. It learns to mimic human perceptual judgments by ranking synthetically generated images with controlled degradations, such as texture distortion and boundary artifacts. This reward-guided fine-tuning enables ODDR to close the synthetic-to-real domain gap. Extensive experiments show that ODD achieves strong performance without relying on real-world paired supervision, and ODDR further improves the results, narrowing the gap to fully supervised methods trained on real-world paired data while maintaining higher computational efficiency as a single-step model.

Problem

• shadow removal
• paired dataset
• generalization
• domain gap
• unsupervised

Innovation

• Shadow Removal
• Diffusion Model
• Reward Guidance
• One-step Generation
• Synthetic-to-Real Domain Gap