Enhancing Low-Light Images Using Infrared-Encoded Images
This addresses the problem of poor image quality in low-light conditions for photography and vision applications, but it is incremental as it builds on existing enhancement methods by modifying hardware capture.
The paper tackles low-light image enhancement by removing the in-camera infrared cut-off filter to capture more photons, resulting in improved brightness, contrast, and texture details with better quantitative and qualitative performance on a new dataset.
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/