ULE-Net: Unsupervised Low-Light Enhancement without Paired Data

Deep learning-based methods have achieved remarkable success in the problem of low-light image enhancement. However, previous works mainly focused on model training using paired or unpaired datasets. Are they still competitive in the absence of accurate classification of image light levels? Instead of classifying image data based on illumination, the approach presented in this paper directly uses images with mixed light levels to train the model. We propose a Unsupervised Low-light Enhancement network, dubbed ULE-Net, that inserts a Light Modulation Module (LMM) into the network to dynamically control the light level of the output image during the calculation process. In the training phase, the available light space of the image is traversed to realize the learning of multi-light levels. The binary conversion problem of low-light image to normal image is successfully converted to a discrete/continuous light conversion problem in the image light space. Through extensive experiments, our proposed method outperforms recent methods in various metrics of visual quality. Meanwhile, our enhancement results are likewise competitive with the most state-of-the-art methods when training with the COCO dataset in the field of non-low-light image. Additionally, our approach demonstrates that for the issue of low-light image enhancement, the light level requirement of the training image is completely arbitrary.