PIC-GAN: Symmetry-Driven Underwater Image Enhancement with Partial Instance Normalisation and Colour Detail Modulation

This model solves the problems of insufficient global feature attention, colour distortion, low contrast, and blurred details in previous methods by using a symmetric U-Net architecture and two new modules: the partial instance normalisation (PIN) module and the colour detail modulation (CDM) module. PIC-GAN effectively balances texture enhancement and feature retention while restoring colour details, ensuring excellent image quality. Experimental results on several publicly available underwater image datasets demonstrate the effectiveness of PIC-GAN, which improves PSNR by 3.23 dB and SSIM by 0.06 compared to WaterGAN, highlighting the potential of PIC-GAN as a powerful solution for underwater image enhancement tasks.