一种改进CycleGAN的水下彩色图像增强方法

An Improved Underwater Color Image Enhancement Algorithm Based on CycleGAN

针对基于深度学习方法的水下图像增强只考虑水下图像的RGB颜色特征空间造成的增强效果不理想现象,本文在循环生成对抗网络的基础上改进了一种水下彩色图像增强算法。首先运用循环生成对抗网络在图像的RGB和HSV颜色特征空间进行训练,将图像经过卷积网络下采样提取到的特征送入残差网络和扩展压缩模块,其中扩展压缩模块可以调整图像RGB和HSV通道的权重。预训练好的生成对抗网络作用在成对的水下降质图像与增强后的图像进行监督训练,采用特征融合网络将对抗生成网络输出的RGB和HSV六通道图像融合成RGB三通道图像。实验结果表明,该方法能够有效结合图像的RGB和HSV空间的特征信息,提升水下图像的对比度和亮度,校正水下图像的颜色偏差。

The underwater image enhancement based on the deep learning method considers only the RGB feature space,therefore the image enhancement effect is unsatisfactory.To cope with this problem,this paper proposed an improved underwater color image enhancement algorithm based on the cyclic generative adversarial network(CycleGAN).Both RGB and HSV color spaces of an image are used to train the CycleGAN.The features down-sampled from the CycleGAN are input into the residual network and the expansion compression module to extract useful features.The weights of RGB and HSV spaces are adaptively adjusted in the expansion and compression module.The pre-trained CycleGAN acts on the paired water degraded image and the enhanced image for weakly supervised training.The feature fusion network is adopted to fuse the output of the CycleGAN into three channels of a new RGB image.The experimental results show that the algorithm can effectively combine the feature information on both RGB and HSV spaces,improves the contrast and brightness of the underwater image and corrects its color deviation.