Towards Real-World Shadow Removal with a Shadow Simulation Method and a Two-Stage Framework

Abstract

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://github.com/jianhaogao/Shadow-Removal-with-Two-stage-Framework.