Joint Contrast Enhancement and Noise Suppression of Low-light Images Via Deep Learning

Abstract

The captured images suffer from low sharpness, low contrast, and unwanted noise when the imaging device is used in conditions such as backlighting, overexposure, or darkness. Learning-based low-light enhancement methods have robust feature learning and mapping capabilities. Therefore, we propose a learning-based joint contrast enhancement and noise suppression method for low-light images (termed JCENS). JCENS is mainly composed of three subnetworks: the low-light image denoising network (LDNet), the attention feature extraction network (AENet), and the low-light image enhancement network (LENet). In particular, LDNet produces a low-light image devoid of unwanted noise. AENet mitigates the impact of LDNet’s denoising process on local details and generates attention enhancement features. Ultimately, LENet combine the outputs of LDNet and AENet to produce a noise-free and normal-light-enhanced image. The proposed joint contrast enhancement and noise suppression network is capable of achieving a balance between brightness enhancement and noise suppression, thereby better preserving salient image details. Both synthetic and realistic experiments have demonstrated the superior performance of our JCENS in terms of quantitative evaluations and visual image qualities.