LIIA -Net: A lightweight illumination iterative adjustment network for low-light image enhancement

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.