Abstract: To overcome the problems of low contrast and color deviation in underwater images, a parallel guided underwater image enhancement algorithm based on Transformer and convolutional neural networks (CNN) is proposed. Using a 3D position embedding model to provide Transformer with relative position information, color deviation information, and global features of feature maps, using a CNN encoder to extract local features of the image, integrating the global features extracted by Transformer and the local features extracted by CNN through a feature modulation matrix, improving the resolution of the image through a CNN decoder, and inputting the feature maps output by the decoder into a feature enhancement network, enhance the network output with features to obtain the final result. Using the existing EUVP paired dataset for training, to verify the superiority of the algorithm, underwater images with varying degrees of color deviation are selected for qualitative and quantitative experiments. The results show that the enhanced underwater image peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are higher than other comparison algorithms, and the subjective quality is significantly improved, the proposed algorithm can generate enhanced images with rich colors and high clarity.

Key words: underwater image enhancement, Transformer, convolutional neural networks (CNN), 3D position embedding model, characteristic modulation matrix

摘要： 为克服水下图像对比度低和色偏的问题，提出了基于Transformer与CNN并行引导的水下图像增强算法。利用3D位置嵌入模型为Transformer提供相对位置信息、色偏信息和特征图的全局特征，利用CNN编码器提取图像局部特征，将Transformer提取的全局特征和CNN提取的局部特征通过特征调制矩阵整合在一起，通过CNN解码器提高图像的分辨率，将解码器输出的特征图输入到特征加强网络中，由特征加强网络输出最终结果。采用现有的EUVP配对数据集进行训练，为验证该算法的优越性，选取具有不同程度色偏的水下图像进行定性比较和定量实验，结果显示，该算法增强后的水下图像峰值信噪比指标（peak signal-to-noise ratio,PSNR）和结构相似性指标（structural similarity index measure，SSIM）均高于其他对比算法，主观质量也得到显著提高，能够产生颜色丰富且清晰度较高的增强图像。

关键词: 水下图像增强, Transformer, 卷积神经网络（CNN）, 3D位置嵌入模型, 特征调制矩阵