Underwater image enhancement based on color correction and TransFormer detail sharpening
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.
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