Collaborative Reflectance-And-Illumination Learning For High-Efficient Low-Light Image Enhancement
Guijing Zhu, Long Ma, Risheng Liu, Xin Fan, Zhongxuan Luo · 2021
In this paper, we settle the low-light image enhancement problem by developing a collaborative learning framework, which not only improves lightness and suppresses noises simultaneously but also with fast speed and requires few computational resources. The approach is inspired by the fact that reflectance and illumination are highly correlated to satisfy the well-known Retinex decomposition principle. With this in mind, we establish a Reflectance-and-Illumination Collaborative (RIC) block to depict the compact physical relationship between reflectance and illumination. By cascading multiple RIC blocks, we obtain an end-to-end RICNet to interactively optimize these two components in a collaborative manner. Benefiting from the RIC block that integrates powerful task cues, RICNet just needs few parameters to simultaneously improve brightness and remove noises. Extensive experiments demonstrate our superiority against existing state-of-the-art methods. We also make meticulous analysis for the RIC block. The results reveal the rationality and effectiveness of our built mechanism.