AASFNet: Amplitude-Aware Spatial-Frequency Network for Low-Light Image Enhancement
TL;DR
The proposed AASFNet achieves competitive or leading performance across multiple key metrics under the evaluated experimental settings, yielding the best or second-best PSNR, SSIM, and NIQE values among the compared methods.
Abstract
Low-light images often suffer from uneven illumination, resulting in reduced brightness, low contrast, and increased noise interference. However, existing enhancement methods frequently lead to over- or under-enhancement, frequency domain distortion, or insufficient noise suppression, which adversely affect both visual quality and the performance of downstream vision tasks. To address these issues, we propose AASFNet, a novel low-light image enhancement network that integrates spatial and frequency domain features through amplitude enhancement and dual-domain fusion. The framework consists of a Frequency-domain Enhancement Network (FreqEnhanceNet) with an illumination-guided Amplitude Module (AmpModule) for adaptive brightness adjustment and a Spatial-Frequency Fusion Network (SpatFreqFusionNet) incorporating a Dual-Domain Fusion Module (DDFModule) for noise suppression and illumination correction via multi-scale feature interaction. This design enables the network to simultaneously adjust global illumination, restore local details, and suppress noise. Additionally, a joint spatial-frequency loss function is introduced, including an illumination-guided amplitude loss and a cosine phase loss, to enhance structural consistency and amplitude fidelity. Comprehensive experiments on the LOL-v2 Real and Synthetic datasets demonstrate that AASFNet achieves competitive or leading performance across multiple key metrics under the evaluated experimental settings, yielding the best or second-best PSNR, SSIM, and NIQE values among the compared methods. Moreover, when applied as a preprocessing module, it achieves improved detection accuracy across multiple low-light benchmarks, with mAP scores of 78.63% on ExDark, 78.2% on DarkFace, and 80.32% on LoLI-Street. These results suggest the potential applicability of the model in real-world scenarios such as autonomous driving and surveillance, although further validation under actual deployment conditions is still required.