Attention-based dual-color space fusion network for low-light image enhancement

Abstract

The low visibility and dull colors associated with low-light images are not only difficult to satisfy the photographer, but also hinder further visual tasks. In this study, we propose an attention-based dual-color space fusion network to enhance low-light images. By introducing HSV color space, the network can solve the problems of dim color and insufficient contrast existing in previous RGB color space methods. In both color spaces, the network adopts a large number of attention mechanisms to highlight important features in real time. To better achieve feature fusion across color space, we design adaptive large-kernel attention (ALKA) and feature extraction module (FEM), respectively. The ALKA adaptively selects features for enriching the encoding input in the HSV color space during the RGB color space encoding process. The FEM assists the network in more comprehensive supervision of the enhancement process and generates the attention map to transmit the marking features in the previous color space. At the end of the network, we also design channel enhancement module (CEM) to protect the texture details lost during the scale change. We have conducted experiments on a variety of public datasets, and both subjective and objective comparisons have demonstrated the particularity and prominence of our method.