HFMNet: Hierarchical Feature Mining Network for Low-Light Image Enhancement

Images captured in low-light environments often suffer from issues related to dark illumination and damaged details, which results in poor visibility. To address these problems, existing methods have attempted to enhance the visibility of low-light images using convolutional neural networks (CNNs). However, due to the insufficient consideration of crucial features such as illumination and edge details, most of them yield unnatural illumination and blurry details. In this work, to fully exploit these features, we present a detailed analysis of the illumination and edge features of low-light images, observing that the frequency components of these two features are considerably different. Therefore, we explore the frequency distributions of the feature maps extracted from different layers of a CNN model and try to seek the best representation for the illumination and edge information. Based on this, we present a hierarchical feature mining network (HFMNet) that extracts illumination and edge features in different network layers. Specifically, we build a feature mining attention (FMA) module combined with a hierarchical supervised loss to mine crucial features in appropriate network layer. Since deep hierarchical supervision tends to cause overfitting, we introduce an unpaired adversarial loss for improving the generality of the enhancement model. Through extensive experiments and analysis, we demonstrate the advantages of the proposed network, which achieves the state-of-the-art performance in terms of image quality.

Automated summary

This study addresses the issues of illumination and edge features in low-light images by proposing a hierarchical feature mining network that analyzes frequency distributions to extract crucial information, achieving state-of-the-art performance in terms of image quality through extensive experiments and analysis.