Enhancing Low-Light Images Using Infrared-Encoded Images
Shulin Tian Yufei Wang Renjie Wan Wenhan Yang Alex C. Kot Bihan Wen
Low-light image enhancement task is essential yet challenging as it is
ill-posed intrinsically. Previous arts mainly focus on the low-light images
captured in the visible spectrum using pixel-wise loss, which limits the
capacity of recovering the brightness, contrast, and texture details due to the
small number of income photons. In this work, we propose a novel approach to
increase the visibility of images captured under low-light environments by
removing the in-camera infrared (IR) cut-off filter, which allows for the
capture of more photons and results in improved signal-to-noise ratio due to
the inclusion of information from the IR spectrum. To verify the proposed
strategy, we collect a paired dataset of low-light images captured without the
IR cut-off filter, with corresponding long-exposure reference images with an
external filter. The experimental results on the proposed dataset demonstrate
the effectiveness of the proposed method, showing better performance
quantitatively and qualitatively. The dataset and code are publicly available
at https://wyf0912.github.io/ELIEI/