FLASH COMPENSATED LOW-LIGHT ENHANCEMENT VIA HIERARCHICAL NETWORK PREDICTION

Photography in low-light conditions suffers from dense noise and insufficient light. Flash photography, introducing extra light sources, performs better at suppressing noise and revealing details, while being interrupted by unnatural ambient illumination. This paper offers an analysis of the pros and cons to utilize low-light and flash images for enhancement, which inspires us to design a unified sample-adaptive CNN to capture diverse focuses from different inputs in a complementary way. Specifically, a Flash Compensated Dynamic Filtering Network is proposed to utilize the revealed details of flash images to compensate for fine structure reconstruction in low-light enhancement. To adaptively fuse information from misaligned low-light and flash image pairs, our network is designed with three distinctive features. Firstly, we adopt a layer-wise regression strategy, where results are predicted from the single input first and then fused to sufficiently leverage complementary information. Secondly, we employ a sample-adaptive mechanism, where each pixel is estimated with its distinctive parameters augmented by weighted residual connections. Finally, we utilize a coarse-to-fine architecture, where features are extracted by diversified receptive fields to utilize hierarchical contextual information. Experimental results demonstrate that the three design principles lead to the significant superiority of the proposed method over state-of-the-art methods.