基于色彩校正和TransFormer细节锐化的水下图像增强

Underwater image enhancement based on color correction and TransFormer detail sharpening

针对水下图像对比度低、细节表现差且存在色偏等问题,提出了一种多输入的基于TransFormer和卷积神经网络(CNN)的水下图像复原方法。利用TransFormer和相对总变差(RTV)构造深度特征提取模块,融合RTV提取的纹理图与TransFormer提取到的图像信息,有效增强了图像的细节特征。利用自动色彩均衡和Lab色彩空间构建色彩校正模块,提升图像对比度,同时校正颜色。利用多项损失函数约束网络收敛,得到增强后的清晰水下图像。最后,将本文方法与其他方法在测试集上进行定量和定性对比分析,实验结果表明,经过本文方法处理后的图像在清晰度、色彩表现和纹理信息方面均优于其他对比方法。

A multi-input underwater image recovery method based on TransFormer and convolutional neural network(CNN)was proposed to address the issues of low contrast,poor detail representation and color error in underwater images.TransFormer and relative total variatio were used to construct a depth feature extraction module to fuse the texture map extracted by relative total variatio(RTV)with the image information extracted by TransFormer,which effectively enhances the detail features of the image.The color correction module was constructed by using automatic color equalization and Lab color space to enhance the image contrast and correct the color.A multinomial loss function was used to constrain the network convergence to obtain the enhanced clear underwater images.Finally,the quantitative and qualitative comparative analysis of the proposed method with other methods on the test set was carried out,and the experimental results show that the images processed by the proposed method outperform other comparative methods in terms of sharpness,color performance and texture information.