Mixture of Gaussians based robust sparse representation for erratic noise suppression

Author(s):  
Weiwei Xu ◽  
Yanhui Zhou ◽  
Xiaokai Wang ◽  
Wenchao Chen
2017 ◽  
Vol 62 (8) ◽  
pp. 379-383 ◽  
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomaryov ◽  
V. I. Pustovoit ◽  
A. Palacios-Enriquez

2012 ◽  
Vol 6-7 ◽  
pp. 682-687
Author(s):  
Bao Ping Wang ◽  
Chao Sun ◽  
Jun Jie Guo

ISAR imaging algorithm based on sparse representation has the advantages of high resolution, noise suppression and dealing with gapped data effectively. The method is based on the hypothesis that the imaging targets move smoothly. But the movement of ISAR imaging targets is usually of high maneuverability, which results in big phase error after motion compensation. Using the traditional RD imaging algorithm and the imaging algorithm based on sparse representation will make the resultant image fuzzy, and can't even be identified. This paper introduces a new range- instantaneous Doppler imaging algorithm based on sparse representation and time-frequency transform, which can effectively image the maneuvering target. The experimental results validate the feasibility of this approach.


2020 ◽  
Author(s):  
Weiwei Xu ◽  
Yanhui Zhou ◽  
Xiaokai Wang ◽  
Wenchao Chen

2000 ◽  
Author(s):  
Edward Awh ◽  
John Serences ◽  
Kelsey Libner ◽  
Michi Matsukura

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.


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