A high-low frequency guided generative adversarial network for shadow removal

The occurrence of shadows in images leads to a damage in image quality and also hinders the performance of downstream visual tasks. Despite significant progress in existing shadow removal methods, they are limited in synthesizing highfrequency signal details, resulting in insufficient synthesis quality. Among them, most of methods based on Generative Adversarial Networks (GANs), while capable of removing shadows, often introduce image artifacts and blurring, particularly underperforming in the processing of high-frequency signals, which impacts the clarity of the image after shadow removal. To address these issues, we propose a high-low frequency guided GAN method for image shadow removal. Concretely, our method decomposes extracted features into multiple frequency components (i.e., low and high frequency) and retains the image's contours and structural information through low-frequency attention skip connections. Meanwhile, high-frequency attention skip connections are employed to alleviate the difficulty for the generator to synthesize details, providing the generator with rich frequency information. Additionally, we also introduce a wavelet-based frequency loss function to reduce the discrepancies between the generated image and the real image in the frequency domain, effectively mitigating the occurrence of artifacts and blurring. Finally, extensive experiments conducted on the ISTD, AISTD, and SRD datasets demonstrate the significant effectiveness of our proposed method, which not only enhances the efficiency of shadow removal but also markedly improves the high-frequency detail quality of the de-shadowed images.