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

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