Efficient adaptive feature aggregation network for low-light image enhancement

Abstract

Existing learning-based methods for low-light image enhancement contain a large number of redundant features, the enhanced images lack detail and have strong noises. Some methods try to combine the pyramid structure to learn features from coarse to fine, but the inconsistency of the pyramid structure leads to luminance, color and texture deviations in the enhanced images. In addition, these methods are usually computationally complex and require high computational resource requirements. In this paper, we propose an efficient adaptive feature aggregation network (EAANet) for low-light image enhancement. Our model adopts a pyramid structure and includes multiple multi-scale feature aggregation block (MFAB) and one adaptive feature aggregation block (AFAB). MFAB is proposed to be embedded into each layer of the pyramid structure to fully extract features and reduce redundant features, while the AFAB is proposed for overcome the inconsistency of the pyramid structure. EAANet is very lightweight, with low device requirements and a quick running time. We conducted an extensive comparison with some state-of-the-art methods in terms of PSNR, SSIM, parameters, computations and running time on LOL and MIT5K datasets, and the experiments show that the proposed method has significant advantages in terms of comprehensive performance. The proposed method reconstructs images with richer color and texture, and the noises is effectively suppressed.

Semi-supervised atmospheric component learning in low-light image problem

“…Usual supervised methods focus on solving the low-light enhancement problem through an end-to-end approach or by using theoretical schemes such as retinex decomposition. In the first category, researchers propose stable networks and customized loss functions [1,9,[29][30][31][32][33], and in the second, two different objectives for reflectance and illumination are solved using novel architectures [34][35][36].…”