Dual-branch underwater image enhancement algorithm based on red channel prior

Aiming at the problems of color distortion, low contrast and detail blur caused by light absorption and scattering in water, an underwater image enhancement algorithm based on red channel prior was proposed. Combining the traditional red channel prior theory with the generation adversarial network, a red channel prior module was designed to pay attention to the attenuation characteristics of the red channel in underwater images and conduct joint training with the encoder of the generator, so that the encoder could use the information provided by the red channel prior module to extract useful features better, which was conducive to solving the color bias problem of underwater images. The discriminator adopted global-local double discriminator, and constructed several loss functions to make the generated image consistent with the reference image in structure, content and color. Compared with other 10 algorithms, the UIEB, EUVP and LSUI public data sets obtained optimal or sub-optimal results. The enhanced underwater image peak signal-to-noise ratio (PSNR), structural similarity (SSIM), underwater image quality metric (UIQM), underwater color image quality assessment (UCIQE) and information entropy(IE) on the UIEB dataset were increased by 1.78 dB, 0.073, 0.016, 0.004 and 0.02, respectively. Mean square error (MSE) decreased by 48.63. The algorithm proposed in this paper has achieved remarkable results in improving the quality of underwater images, showing obvious advantages in both subjective visual perception and objective data.