Semantically Contrastive Learning for Low-light Image Enhancement

TL;DR

This paper introduces SCL-LLE, a semantically contrastive learning framework that enhances low-light images by leveraging unpaired images and semantic guidance, improving performance across multiple datasets.

Contribution

It proposes a novel multi-task learning paradigm combining contrastive learning, semantic brightness consistency, and feature preservation for low-light image enhancement.

Findings

Outperforms state-of-the-art LLE models on six datasets

Effectively leverages unpaired over/underexposed images and semantic information

Improves downstream semantic segmentation in dark conditions

Abstract

Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of cutting-edge LLE models? Here, we propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image enhancement task as multi-task joint learning, where LLE is converted into three constraints of contrastive learning, semantic brightness consistency, and feature preservation for simultaneously ensuring the exposure, texture, and color consistency. SCL-LLE allows the LLE model to learn from unpaired positives (normal-light)/negatives (over/underexposed), and enables it to interact with the scene semantics to regularize the image enhancement network, yet the interaction of high-level semantic knowledge and the low-level signal prior is seldom investigated in previous methods. Training on readily available open data, extensive experiments demonstrate that our method surpasses the state-of-the-arts LLE models over six independent cross-scenes datasets. Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation under extremely dark conditions is discussed. Source Code: https://github.com/LingLIx/SCL-LLE.