To address the overall bluish and greenish tones,blurred edge details and low contrast of underwater images received by underwater robots in unrestricted environments during underwater operations,an underwater image enhancement algorithm is proposed based on grey-scale world white balance and end-to-end gated contextual aggregation network.Firstly,the grey-scale world algorithm is designed to adjust the R,G and B components of the underwater image to obtain a colour-corrected underwater image.Secondly,the corrected underwater image is fed into a gated contextual aggregation network,and the gated network is used to fuse the features at different levels in the image,and the smooth cavity technique and feature attention module are introduced to eliminate the grid artefacts caused by the cavity convolution and improve the channel information flexibility to achieve the effect of image enhancement.Finally,1 000 images with reference are selected and compared with six classical enhancement algorithms for subjective and objective evaluation.The results show that,the method proposed in this paper improves the contrast and sharpness of the underwater enhanced image in terms of subjective quality,and correct the color deviation of the underwater images.In terms of objective evaluation indicators,in test set A,the average values of peak signal to noise ratio(PSNR),structural similarity(SSIM),information entropy(IE)and underwater color image quality evaluation(UCIQE)reach 25.176 0 dB,0.950 9,8.057 9 and 0.618 2,respectively.In test set B,the average values of PSNR,SSIM,IE and UCIQE reach 21.576 1 dB,0.933 1,8.119 4 and 0.591 4,respectively.All of them achieve superior evaluation results to the six algorithms compared.
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