Advancing Ambient Lighting Normalization via Diffusion Shadow Generation

Xin Lu, Jiarong Yang, Yuanfei Bao, Zihao Fan, Anya Hu, Kunyu Wang, Jie Xiao, Xi Wang, Hongjian Liu, Xueyang Fu, Zheng-Jun Zha; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025, pp. 1070-1080

Ambient Lighting Normalization (ALN) finds extensive applications in images captured in complex real-world scenarios and can be extended to more general image restoration tasks. To address the ALN issue in real-world high-resolution images, this paper proposes Advancing Ambient Lighting Normalization via Diffusion Shadow Generation. Specifically, we design a mask-free shadow removal network based on a U-Net architecture without global residual connections, which can effectively handle a wider range of image restoration tasks and exhibit high throughput during training on large-scale high-resolution images. During the training of the mask-free shadow removal model, we dynamically overlay diverse shadow conditions generated by a diffusion model onto the restored images, facilitating iterative learning within the shadow removal network. This results in an efficient general image restoration network capable of handling high-resolution images under complex ambient lighting conditions. Experimental results on official datasets confirm the superiority of our method.