Polarimetric image denoising on small datasets using deep transfer learning

Although deep learning-based methods have achieved great success in various polarimetric imaging tasks, the performance and the generalization ability are strongly dependent on massive training data, which is a critical limitation and limits the practical applications. In this paper, for the first time to our knowledge, we present a deep transfer learning-based solution for polarimetric image denoising. This solution performs the transfer learning by fine-tuning a denoising model pre-trained on a large-scale color image dataset and using a small-scale polarimetric dataset. The experimental results show that, based on a small-scale dataset, the proposed network can achieve almost the same denoising performance as that with a large-scale dataset. The polarization parameters, i.e., the degree of polarization and the angle of polarization, can be reconstructed simultaneously. In addition, serials of experiments demonstrate the generalization ability of the method for different materials and noise levels.