scholarly journals The application of curvelet transform in noise suppression of airbornemagnetic data

2017 ◽  
Author(s):  
Hailong Sun ◽  
Hongye Wang ◽  
Xin Chen ◽  
Wei Zheng
Keyword(s):  
Noise Suppression ◽  
Curvelet Transform

NOISE SUPPRESSION OF RECEIVER FUNCTIONS USING CURVELET TRANSFORM

2016 ◽  
Vol 59 (2) ◽  
pp. 125-138
Author(s):  
QI Shao-Hua ◽  
LIU Qi-Yuan ◽  
CHEN Jiu-Hui ◽  
GUO Biao
Keyword(s):  
Noise Suppression ◽  
Curvelet Transform ◽  
Receiver Functions

Noise suppression based on a fast discrete curvelet transform

2010 ◽  
Vol 7 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Zhi-yu Zhang ◽  
Xiao-dan Zhang ◽  
Hai-yan Yu ◽  
Xue-hui Pan
Keyword(s):  
Noise Suppression ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

scholarly journals Curvelet Denoising with Improved Thresholds for Application on Ultrasound Images

2013 ◽  
pp. 156-161
Author(s):  
Tajinder Kaur ◽  
Dinesh Kumar ◽  
Ekta Walia ◽  
Manjit Sandhu
Keyword(s):  
Medical Image ◽  
Noise Suppression ◽  
Acoustic Noise ◽  
Medical Image Processing ◽  
Curvelet Transform ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Challenging Problem ◽  
Effective Suppression ◽  
Ultrasonic Images

In medical image processing, image denoising has become a very essential exercise all through the diagnose. Negotiation between the preservation of useful diagnostic information and noise suppression must be treasured in medical images. In case of ultrasonic images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. Many denoising techniques have been proposed for effective suppression of speckle noise. Removing noise from the original image or signal is still a challenging problem for researchers. In this paper, a Curvelet transform based denoising with improved thresholds is proposed for ultrasound images.

Blended noise suppression using a hybrid median filter, normal moveout and complex curvelet transform approach

2020 ◽  
Vol 64 (2) ◽  
pp. 241-254
Author(s):  
Lieqian Dong ◽  
Changhui Wang ◽  
Mugang Zhang ◽  
Deying Wang ◽  
Xiaofeng Liang
Keyword(s):  
Noise Suppression ◽  
Median Filter ◽  
Curvelet Transform

Endogenous Noise Suppression During Covert Orienting

2000 ◽  
Author(s):  
Edward Awh ◽  
John Serences ◽  
Kelsey Libner ◽  
Michi Matsukura
Keyword(s):  
Noise Suppression ◽  
Covert Orienting

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

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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