Low-light image enhancement with contrast regularization

Abstract

Because the processing of existing low-light images undergoes multiple sampling processing, there is serious information degradation, and only clear images are used as positive samples to guide network training, low-light image enhancement processing is still a challenging and unsettled problem. Therefore, a multi-scale contrast learning low-light image enhancement network is proposed. First, the image generates rich features through the input module, and then the features are imported into a multi-scale enhancement network with dense residual blocks, using positive and negative samples to guide the network training, and finally using the refinement module to enrich the image details. Experimental results on the dataset show that this method can reduce noise and artifacts in low-light images, and can improve contrast and brightness, demonstrating its advantages.