LIIA -Net: A lightweight illumination iterative adjustment network for low-light image enhancement.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41468869.
- Also identified by DOI 10.1016/j.neunet.2025.108510.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Low-light image enhancement aims to improve brightness, contrast, and structural details of degraded images, thus improving image quality and supporting visual perception tasks. However, existing methods often lead to inaccurate illumination adjustment, amplified noise, and structural loss. To address these issues, we propose a Lightweight Illumination Iterative Adjustment Network (LIIA-Net) that jointly processes images in both frequency and spatial domains. First, a linear cross attention module fuses illumination and content features. Then, an amplitude adaptive iterative adjustment module adaptively regulates brightness in the frequency domain. Finally, a mamba-based structure refinement module restores spatial textures. Despite having only 0.48M parameters, LIIA-Net achieves performance comparable to or even surpassing state-of-the-art methods on both real and synthetic datasets. Moreover, when applied to downstream object detection, our enhanced images significantly boost detection accuracy. The code is available at https://github.com/ycwsilent/LIIANet.