Lightweight Attention-CycleGAN for Nighttime-Daytime Image Transformation

Junhao Huang, Xiangjun Xiao, Haojun Zhou, Affan Yasin, Zhili Zhou

Abstract: With the rapid development of deep learning in the field of computer vision, the performance of core vision tasks such as image recognition has achieved significant improvement. In nighttime environment, due to the low-light condition and reduced visibility, cross-domain transformation of nighttime images based on Generative Adversarial Network (GAN) model can effectively improve the accuracy of nighttime recognition models. However, the existing GAN models are difficult to be effectively deployed on resource-constrained devices due to the requirement of high storage space and computational resource. To this end, this paper proposes a shared attention network based on the attention mechanism with the CycleGAN structure, and designs an online knowledge distillation method to compress and optimize the model, so as to obtain a lightweight model to achieve the nighttime-daytime cross-domain image transformation. Experimental results demonstrate that the proposed model achieves the state-of-the-art performance in the task of Nighttime-Daytime Image Transformation. This is of great significance for edge devices to solve the problem of recognition at night.

External IDs:dblp:conf/aisp/HuangXZYZ23