CUCN: continuously updated connection network for low-light image enhancement

Abstract

Low-light (LL) images make subsequent computer vision tasks difficult due to its low contrast. To solve this problem, the LL image enhancement problem is regarded as narrowing the gap between LL images and normal-light images in the process of iterative learning. A continuously updated connection network (CUCN) that is inspired by the recent development of convolutional neural networks is proposed. The proposed CUCN is composed of a continuously updated 4-units module (CU4UM), a feature fusion module (FFM), and a color enhancement module (CEM). The CU4UM adopts a connection method to combine four U-shaped structures with continuously updated parameters. To retain the global and local details while brightening an image, the FFM effectively combines the features of different layers in different U-shaped structures and assigns appropriate weights to each channel. The CEM takes the residuals of multiple features of different scales and performs iterative weighting to obtain more natural color information. The proposed method is evaluated on different public datasets, and the results show that the proposed CUCN method is superior to other state-of-the-art methods in terms of both subjective and objective metrics.