Mueller transform matrix neural network for underwater polarimetric dehazing imaging

Abstract

Polarization dehazing imaging has been used to restore images degraded by scattering media, particularly in turbid water environments. While learning-based approaches have shown promise in improving the performance of underwater polarimetric dehazing, most current networks rely heavily on data-driven techniques without consideration of physics principles or real physical processes. This work proposes, what we believe to be, a novel Mueller transform matrix network (MTM-Net) for underwater polarimetric image recovery that considers the physical dehazing model adopting the Mueller matrix method, significantly improving the recovery performance. The network is trained with a loss function that combines content and pixel losses to facilitate detail recovery, and is sped up with the inverse residuals and channel attention structure without decreasing image recovery quality. A series of ablation experiment results and comparative tests confirm the performance of this method with a better recovery effect than other methods. These results provide deeper understanding of underwater polarimetric dehazing imaging and further expand the functionality of polarimetric dehazing method.