MAF-NET: A Multi-Scale Feature Fusion Network for Low-Light Driving Image Enhancement

In low-light driving scenarios, in-vehicle camera images encounter technical challenges, including severe brightness degradation and short exposure times. Conventional driving image enhancement algorithms are susceptible to issues such as the loss of image features and significant color distortion. The proposed solution to this problem is a multi-scale attention fusion network (MAF-NET) for the enhancement of images captured during low-light driving conditions. The network’s structural design is uncomplicated. The model incorporates a meticulously designed multi-scale attention fusion module (MAFB), along with all essential components for network connectivity. The MAF is predicated on a heavy parameter residual feature block design and incorporates a multi-scale channel attention mechanism to capture richer global/local features. A substantial body of experimental evidence has demonstrated that, in comparison with prevailing algorithms, MAF-NET exhibits superior performance in low-light enhancement, detail retention, and color reproduction. Moreover, it attains commendable results in both subjective visibility assessments of nighttime driving scenarios and objective image quality metric tests, such as PSNR and SSIM.