LTNet: Light Transfer Network for Depth Guided Image Relighting

Yu Zhu, Bosong Ding, Chenghua Li, Wanli Qian, Fangya Li, Yiheng Yao, Ruipeng Gang, Chunjie Zhang, Jian Cheng

Abstract: Relighting is an interesting yet challenging low-level vision problem, which aims to re-render the scene with new light sources. In this paper, we introduce LTNet, a novel framework for image relighting. Unlike previous methods, we propose to solve this challenging problem by decoupling the enhancement process. Specifically, we propose to train a network that focuses on learning light variations. Our key insight is that light variations are the critical information to be learned because the scene stays unchanged during the light transfer process. To this end, we employ a global residual connection and corresponding residual loss for capturing light variations. Experimental results show that the proposed method achieves better visual quality on the VIDIT dataset in the NTIRE2021 relighting challenge.