Enhancing Low-Light Images Using Infrared-Encoded Images

TL;DR

This paper introduces a novel low-light image enhancement method that leverages infrared-encoded images captured without an IR cut-off filter, improving visibility by utilizing additional IR spectrum information.

Contribution

It proposes removing the IR cut-off filter to capture more photons, creating a paired dataset, and demonstrating improved enhancement performance over existing methods.

Findings

Enhanced image visibility in low-light conditions

Quantitative and qualitative improvements over prior methods

Public dataset and code available for further research

Abstract

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/