【摘要】 针对水下图像颜色失真、关键信息模糊和细节特征丢失的问题，提出一种基于SK注意力残差网络的水下图像增强方法.该方法通过改进生成对抗网络中的生成器结构，引入残差模块，减少编码器和解码器之间的特征丢失，增强了图像细节和颜色.为了使网络能适应不同尺度的特征图提取图像关键信息，该方法在残差模块后添加SK注意力机制，采用参数修正线性单元来提高网络的拟合能力.将本文方法分别在真实和合成的水下图像数据集中进行验证，采用传统方法和深度学习的方法进行主客观评价.在主观效果分析中发现，本文方法增强后的图像颜色、关键信息和细节特征都有很大提升.在客观评价指标中发现，本文方法指标值均高于现有的水下图像增强算法，验证了该算法的有效性.更多还原

【Abstract】 In order to solve the problems of color distortion, key information blur and detail loss perplexed underwater image, an underwater image enhancement method based on SK attention residual network is proposed.The generator structure in the generative adversarial network is improved, and a residual module is introduced to reduce the feature loss between encoder and decoder, thus enhance the image detail and color.To make the network adapt to different scale feature maps to extract key information of images, the SK attention mechanism is added after the residual module.Meanwhile, a parametric rectified linear unit is used to improve the fitting ability of the network.This method is verified on real and synthetic underwater image datasets, and traditional method and deep learning method are used for subjective and objective evaluations.In the subjective effect analysis, it is found that the color, key information and detail features have been greatly improved in enhanced images.In the objective evaluation, it is found that the indicator values of the proposed method are higher than those of existing underwater image enhancement algorithms, which verifies the effectiveness of this method.更多还原