Unsupervised Low-Light Image Enhancement via Luminance Mask and Luminance-Independent Representation Decoupling

Enhancing low-light images in an unsupervised manner has become a popular topic due to the challenge of obtaining paired real-world low/normal-light images. Driven by massive available normal-light images, learning a low-light image enhancement network from unpaired data is more practical and valuable. This paper presents an unsupervised low-light image enhancement method (DeULLE) via luminance mask and luminance-independent representation decoupling based on unpaired data. Specifically, by estimating a luminance mask from low-light image, a luminance mask-guided low-light image generation (LMLIG) module is presented to darken reference normal-light image. In addition, a luminance-independent representation-based low-light image enhancement (LRLIE) module is developed to enhance low-light image by learning luminance-independent representation and incorporating the luminance cue of reference normal-light image. With the LMLIG and LRLIE modules, a bidirectional mapping-based cycle supervision (BMCS) is constructed to facilitate the decoupling of the luminance mask and luminance-independent representation, which further promotes unsupervised low-light enhancement learning with unpaired data. Comprehensive experiments on various challenging benchmark datasets demonstrate that the proposed DeULLE exhibits superior performance.