Maritime image deraining algorithm based on improved MPRNet

When ships navigate, they often encounter rain, which can reduce the resolution of both onboard intelligent perception systems and shore-based monitoring systems. This reduction compromises ship target detection accuracy, posing a significant threat to maritime safety. To address this issue and ensure safe maritime navigation, this study introduces an enhanced maritime image deraining model based on MPRNet. By improving image quality during rainy conditions, the model supports subsequent ship target detection tasks, ensuring safe vessel navigation. In this study, MPRNet is used as the baseline model. A Pixel Convolution (PConv) structure is added after each stage's channel attention module, creating the Pixel and Channel Attention Block (PCAB) to effectively reduce residual raindrops in images under heavy rain. Additionally, a Total Variation Regularizer Term is included in the loss function to smooth derained images, addressing coarse details. Extensive comparative experiments show that the proposed model significantly outperforms existing models. Compared to the baseline, the proposed model increases PSNR by 2.24 and improves SSIM by 0.045.