Underwater image enhancement based on Inception-Residual and generative adversarial network

To solve the blurring, low contrast and color distortion problem of underwater image caused by light absorption and scattering effects in the underwater environment, an underwater image enhancement algorithm based on the Inception-Residual and generative adversarial network is proposed. Firstly, the degraded underwater image is scaled to a size of 256 x 256 x 3 to obtain a data set for the training model. The Inception module, residual idea, encoding and decoding structure and generative adversarial network are combined to build an IRGAN(Generative Adversarial Network with Inception-Residual) model to enhance underwater images. Then, a multi-loss function including global similarity, content perception and color perception is constructed to constrain the antagonistic training of generative network and discriminant network. Finally, the degraded underwater image is processed by the trained model to obtain a clear underwater image. The experimental results show that, compared with the existing enhancement methods, the average values of the PSNR, UIQM and IE indicators of the underwater images enhanced by the proposed algorithm are improved by 13.6%, 4.1% and 0.9%, respectively, compared with the second place. In subjective perception and objective evaluation, the sharpness, contrast enhancement and color correction of the enhanced underwater image are improved.