Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (1): 81-91.doi: 10.23919/JSEE.2021.000009

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

SAR image de-noising via grouping-based PCA and guided filter

Jing FANG1,2,*(), Shaohai HU1,*(), Xiaole MA1()   

  1. 1 Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
    2 Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China
  • Received:2018-03-12 Online:2021-02-18 Published:2021-03-16
  • Contact: Jing FANG,Shaohai HU E-mail:fangjing@sdnu.edu.cn;shhu@bjtu.edu.cn;maxiaole@bjtu.edu.cn
  • About author:|FANG Jing was born in 1980. She received her M.S. and Ph.D. degrees in Institute of Information Science from Beijing Jiaotong University, in 2005 and 2020, respectively. She has been a lecturer in Shandong Normal University since 2008. She has co-authored more than 15 journal articles and conference proceedings. Her research interests include SAR image processing and signal processing. E-mail: fangjing@sdnu.edu.cn||HU Shaohai was born in 1964. He received his B.S. and M.S. degrees in the Department of Electronic Engineering from Beihang University in 1985 and 1988, respectively. He received his Ph.D. degree in Institute of Information Science from Beijing Jiaotong University in 1991 and has been a professor of Institute of Information Science since 2008. He has co-authored more than 100 journal articles and conference proceedings, and has published three books in his research area. His research interests include signal processing and information fusion, including image fusion, image denosing and sparse representation. E-mail: shhu@bjtu.edu.cn||MA Xiaole was born in 1991. She received her B.S. degree in communication engineering at Electronic Information Engineering from Hebei University in 2015, and Ph.D. degree in Institute of Information Science from Beijing Jiaotong University in 2020. She had been a visiting scholar at University of Missouri-Columbia from 2018 to 2019. Currently, she is a postdoctoral fellow in Institute of Information Science from Beijing Jiaotong University. Her research interests include image fusion and sparse representation. E-mail: maxiaole@bjtu.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62002208; 61572063; 61603225), and the Natural Science Foundation of Shandong Province (ZR2016FQ04)

Abstract:

A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.

Key words: synthetic aperture radar (SAR) image de-noising, local pixel grouping (LPG), principal component analysis (PCA), guided filter