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 int

SUBMITTER: Li C 

PROVIDER: S-EPMC9398031 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature