基于生成式对抗网络和多级小波包卷积网络的水下图像增强算法

为了解决水下图像的雾模糊和偏色问题，针对水下图像成像模型提出基于生成式对抗网络（GAN）和改进卷积神经网络（CNN）的水下图像增强算法. 利用生成式对抗网络合成水下图像，以对配对式水下图像数据集进行有效扩充. 利用多级小波变换，以不丢失特征分辨率的方式对水下图像进行多尺度分解，然后结合卷积神经网络利用紧凑式学习方式对多尺度图像进行特征提取，并利用跳跃连接以防止梯度弥散，克服水下图像的雾模糊效应. 利用风格代价函数学习彩色图像各通道间的相关性，提高模型的色彩校正能力，克服水下图像色彩失真的问题. 实验结果表明，相较对比算法，在主观视觉和客观指标上，本研究所提算法拥有更优秀的综合性能及鲁棒性.

Underwater image enhancement algorithm based on GAN and multi-level wavelet CNN

An underwater image enhancement algorithm was proposed based on generative adversarial networks (GAN) and improved convolutional neural networks (CNN) in order to solve the problems of haze blurring and color distortion of underwater image. Generative adversarial network was used to synthesize underwater images to effectively expand the paired underwater data set. The underwater image was decomposed by multi-scale wavelet transform without losing the feature resolution. Then, combined with CNN, the compact learning method was used to extract features from multi-scale images, and skip connection was used to prevent gradient dispersion. Finally, the fog blur effect of the underwater image was resolved. In order to improve the color correction ability of the model and overcome the problem of color distortion of underwater images, the correlation between different channels of color images was learned by using the style cost function. Experimental results show that, in subjective visual and objective indicators, the proposed algorithm is superior to the contrast algorithm in comprehensive performance and robustness.