FW-GAN: Underwater image enhancement using generative adversarial network with multi-scale fusion

Highlights：

• We propose a decoder model combined with channel attention to compute the attention of the prior and decoded feature maps in the fusion process and adjust them adaptively. The aim is to learn the potential associations between the priori features of fusion and the enhanced results.

• We conducted qualitative and quantitative evaluations and compared FW-GAN with traditional methods and state-of-the-art models. The results show that FW-GAN has good generalization capability and competitive performance. Finally, we conduct an ablation study to demonstrate the contribution of each core component in our network.

摘要

•We propose a multi-scale fusion generator network architecture. Based on the analysis of the underwater environment, an adaptive fusion strategy is proposed to fuse the multi-source and multi-scale features, which can effectively correct the color casts and haze of the image and improve its contrast, it can also avoid blind enhancement of the image and improve the generalization capability of the model.•We propose a decoder model combined with channel attention to compute the attention of the prior and decoded feature maps in the fusion process and adjust them adaptively. The aim is to learn the potential associations between the priori features of fusion and the enhanced results.•We conducted qualitative and quantitative evaluations and compared FW-GAN with traditional methods and state-of-the-art models. The results show that FW-GAN has good generalization capability and competitive performance. Finally, we conduct an ablation study to demonstrate the contribution of each core component in our network.