Bilevel Learning for Low-Light Image Enhancement and Detection

Abstract

Object detection in low-light scenes is a challenging but widely discussed topic in computer vision. A common approach in low-light object detection involves employing cascaded architectures to connect enhancement and detection networks. This strategy aims to bridge the gap between low-light and normal-light images in both the image and feature domains, enabling the effective application of existing object detection networks to low-light scenarios. However, the cascade architecture ignores the intrinsic connection between low-light image enhancement and object detection tasks. To address this problem, we use a bilevel learning architecture to bridge these two tasks. Specifically, we take the parameters of the enhancement network as the main optimization objective and the parameters of the object detection network as the learnable constraints. In addition, to improve the practicality, we propose a method based on implicit function theory to approximate the solution of the corresponding gradient. Numerous experiments have demonstrated that our method significantly improves enhancement quality and detection accuracy.