Abstract
Images captured at night or under low-light conditions often suffer from insufficient brightness, low resolution, and detail loss. Although numerous deep learning-based methods have been proposed, most rely on direct mappings from low-illumination to normal-illumination images, which struggle to adapt to diverse real-world conditions. To address these challenges, this paper proposes a Multi-Branch Low-Light Image Iterative Enhancement Network (MBLLIE-Net). Specifically, to enhance feature extraction at different levels, our framework adopts a multi-branch architecture, in which features of various depths and scales extracted by the encoder are processed and refined through multiple parallel branches. To overcome the limitation of insufficient spatial dependency modeling, we introduce a Spatial Recurrent Unit (SRU) within each branch, which effectively captures long-range spatial relationships while preserving local details. Furthermore, to better emphasize salient channels across varying feature dimensions, we propose an Adaptive Receptive Field Channel Attention (ARFCA) module that dynamically adjusts its receptive field according to the channel dimension, enabling precise feature selection with negligible computational overhead. Finally, the decoder fuses the outputs from all branches to generate an initial enhanced result, which is iteratively refined by concatenating it with the original input, ensuring progressive improvement in image quality. Extensive experiments demonstrate that MBLLIE-Net effectively restores illumination, detail, and color fidelity across a wide range of low-light scenarios, outperforming existing single-path approaches in both quantitative metrics and human perceptual evaluations.