Multi-Resolution Edge-aware Lighting Enhancement Network.

Images taken under poor lighting conditions generally present problems such as low brightness, noise, and color distortion, so dealing with low-light images is challenging. To solve this problem, current mainstream methods focus on designing special deep networks. For example, many methods are based on Retinex-based models. However, the solution of the Retinex model is an ill-conditioned problem in mathematics, so these methods are difficult to obtain the expected result. In this paper, following the new paradigm of embedding prior knowledge in deep learning, we design a simple yet surprisingly effective neural network, called Multi-Resolution Edge-aware Lighting Enhancement Network (MELENet). Since resolution and acutance are two of the key factors related to the visual quality of natural images, MELENet with a dual-branch structure aims to incorporate multi-resolution edge information into deep learning for guiding lighting enhancement. Additionally, by adopting special loss functions, the model suppresses noise and prevents color distortion while obtaining sound lighting enhancement effects. Experiments have verified that such a simple solution exhibits outstanding performance compared to existing mainstream lighting enhancement methods. We also directly apply the proposed model to image de-hazing and obtain state-of-the-art results. [Display omitted] • The proposed algorithm focuses on embedding prior knowledge into the network to assist the low-light image enhancement. • An effective loss function is designed to balance the color distortion and noise suppression. • Our model can be directly applied to other image processing tasks such as image de-hazing and gain competitive results. [ABSTRACT FROM AUTHOR]