A Learning-Based Multi-Type Noise Suppressing Method for Remote Sensing Images
Yuhui Li, Xindi Yu, Jifang Pei, Weibo Huo, Yin Zhang, Yulin Huang, Jianyu Yang · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Remote sensing images (RSIs) play an important role in a wide range of applications. However, they are frequently contaminated by multiple kinds of noises and existing methods are mostly applied to suppressing single noise type and performs poorly for various noises. To deal with above deficiencies, we propose a learning-based multi-type noise suppressing method (MNSM). Firstly, “Parallel” denoising approach is utilized to obtain partially denoised images that supply sufficient information for the subsequent fusion task. Mean-while, the noise recognition net identifies noise type and adjusts the brightness of every partially denoised image, realizing the adaptivity for different noises. The fusion net lastly merges these images to acquire one clean image. Experimental results show that this approach obtains higher peak-signal-to-noise ratio (PSNR) than existing methods.