Learning-based denoising for polarimetric images
Xiaobo Li, Haiyu Li, Lin Yang, Jianhua Guo, Jingyu Yang, Huanjing Yue, Kun Li, Chuan Li, Zhenzhou Cheng, Haofeng Hu, Tiegen Liu
Abstract
Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising. This method is based on the residual dense network and can significantly suppress the noise in polarimetric images. The experimental results show that the proposed method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.