Full scene underwater imaging with polarization and an untrained network

Abstract

The veiling effect caused by the scattering and absorption of suspending particles is a critical challenge of underwater imaging. It is possible to combine the image formation model (IFM) with the optical polarization characteristics underwater to effectively remove the veiling effect and recover a clear image. The performance of such methods, to a great extent, depends on the settings of the global parameters in the application scenarios. Meanwhile, learning-based methods can fit the underwater image information degradation process nonlinearly to restore the images from scattering. Here, we propose for the first time a method for full scene underwater imaging that synergistically makes use of an untrained network and polarization imaging. By mounting a Stokes mask polarizer on the CMOS camera, we can simultaneously obtain images with different polarization states for IFM calculation and optimize the imaging automatically by an untrained network without requiring extra training data. This method makes full use of the nonlinear fitting ability of a neural network and corrects the undesirable imaging effect caused by imperfect parameter settings of the classical IFM in different scenes . It shows good performance in removing the impact of water scattering and preserving the object information, making it possible to achieve clear full scene underwater imaging.