IIAG-CoFlow: Inter- and Intra-Channel Attention Transformer and Complete Flow for Low-Light Image Enhancement With Application to Night Traffic Monitoring Images

Changhui Hu, Tiesheng Chen, Donghang Jing, Kerui Hu, Yanyong Guo, Xiaoyuan Jing, Pan Liu

Abstract: This paper proposes a novel normalizing flow learning based method IIAG-CoFlow for low-light image enhancement (LLIE), which consists of an inter-and intra-channel attention Transformer based conditional generator (IIAG) and a complete flow (CoFlow). On the one hand, IIAG is designed as a U-shape network, whose down-sampling and up-sampling layers are constructed by IIZAT (i.e., inter-and intra-channel and zero-map attention Transformer) and IIAT (i.e., inter-and intra-channel attention Transformer) respectively. IIAT is designed to calculate inter-channel attention and intra-channel attention independently. Based on IIAT, IIZAT is designed to perform parallel fusion of zero-map attention and intra-channel attention. On the other hand, based on existing normalizing flow, we bring in unconditional affine coupling layer and design 3 invertible linear transformation layers, to develop CoFlow. The height and width axes based cross attention network (HWCAN) is proposed to learn affine/linear transformation parameters for conditional feature-driven layers of CoFlow. Experiments show that IIAG-CoFlow outperforms existing SOTA LLIE methods on several benchmark low-light datasets, and real NTM images. The source codes and pre-trained models are available at https://github.com/NJUPT-IPR-ChenTS/IIAG-CoFlow.

External IDs:dblp:journals/tits/HuCJHGJL25