Low-light image enhancement via Clustering Contrastive Learning for visual recognition

Abstract

Visual recognition tasks of low-light images remain a big challenge. We propose an unsupervised low-light image enhancement module that can be integrated into any baseline visual model to enhance the performance. The proposed method is based on Clustering Contrastive Learning and Grad-CAM (Gradient-Class Activation Map) feature alignment, called CCGC. The CCGC method enhances the luminance semantic information of low-light images and remains the semantic feature information focusing. Simulation experimental results on various low-light image datasets demonstrate the significant feature enhancement and generalization capability of CCGC. Evaluation of the established CUB-2011 low-light image dataset shows a substantial increase in classification accuracy across multiple benchmark models. Furthermore, the proposed method significantly improves the classification accuracy on a real low-light traditional Chinese medicine dataset and enhances face detection performance on dark face detection datasets.