机械科学与技术2023，Vol.42Issue(12)：2093-2099,7.DOI:10.13433/j.cnki.1003-8728.20220162
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
摘要
Abstract
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.关键词
水下彩色图像增强/循环对抗生成网络/卷积层压缩扩展/颜色空间融合Key words
underwater color image enhancement/cyclic generative adversarial network/convolution layer expansion and compression/color space fusion分类
信息技术与安全科学引用本文复制引用
刘朝,王红茹..一种改进CycleGAN的水下彩色图像增强方法[J].机械科学与技术,2023,42(12):2093-2099,7.基金项目
国家重点研发计划项目(2018YFC0309100) （2018YFC0309100）
