Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement - Cui, HengShuai; Li, Jinjiang; Hua, Zhen; Fan, Linwei (Frontiers in Neurorobotics, 2022)
Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network(MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images. First, to avoid the high cost computation arising from the stacking of multiple sub-networks, our network uses a single encoder and decoder for multi-scale input and output images. Multi-scale input images can make up for the lack of pixel information and loss of feature map information caused by a single input image. The multi-scale output image can effectively monitor the error loss in the image reconstruction process.