BrightsightNet: A lightweight progressive low-light image enhancement network and its application in Rainbowmaglev train

To address the low-light image (LLI) problem in train driving scenarios, this paper proposes a progressive and lightweight network called BrightsightNet for LLI enhancement. First, to overcome the problem of insufficient local exposure level, two structurally identical light curve parameter estimation sub-networks are used for light enhancement in turn. Second, for real-time inference, an efficient feature extraction operator is proposed that combines depth-separable convolution and attention mechanism. Third, the overall network uses encoder- decoder architecture. For the encoder, the output features of the three layers are fused through skip connections to form an information-rich feature map. For the decoder, a hierarchical decoding approach is used to predict the light curve parameters through the three convolution layers sequentially. Experimental results show that BrightsightNet achieves a user study score (USR) of 4.43 on the proposed dataset, outperforming Zero-DCE++, SCI, RetinexDIP, and RUAS by 0.51, 0.86, 0.64, and 1.39, respectively. Moreover, BrightsightNet has parameters of only 2.6K and a single inference time of 0.052 s, which is an innovative and practical solution for low-light image enhancement in train driving scenarios, contributing to safer and more reliable train operations.

Automated summary

This paper proposes a progressive and lightweight network called BrightsightNet for low-light image enhancement in train driving scenarios. It addresses the problem of insufficient local exposure level by using two structurally identical light curve parameter estimation sub-networks and introduces an efficient feature extraction operator. Experimental results show that BrightsightNet achieves excellent performance on the proposed dataset with small parameter size and short inference time.