DANet: A Domain Alignment Network for Low-Light Image Enhancement

Abstract

We propose restoring low-light images suffering from severe degradation using a deep-learning approach. A significant domain gap exists between low-light and real images, which previous methods have failed to address with domain alignment. To tackle this, we introduce a domain alignment network leveraging dual encoders and a domain alignment loss. Specifically, we train two dual encoders to transform low-light and real images into two latent spaces and align these spaces using a domain alignment loss. Additionally, we design a Convolution-Transformer module (CTM) during the encoding process to comprehensively extract both local and global features. Experimental results on four benchmark datasets demonstrate that our proposed A Domain Alignment Network(DANet) method outperforms state-of-the-art methods.