Speckle Noise Removal for SAR Image Based on G0 Distribution Combining Total Variation and Total Curvature

2019 ◽  
Author(s):  
Yunping Mu ◽  
Baoxiang Huang ◽  
Zhenkuan Pan ◽  
Huan Yang ◽  
Yajing Li
Keyword(s):  
Total Variation ◽  
Total Curvature ◽  
Speckle Noise ◽  
Noise Removal ◽  
Sar Image

scholarly journals Speckle Noise Removal by Energy Models with New Regularization Setting

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Bo Chen ◽  
Jinbin Zou ◽  
Weiqiang Zhang
Keyword(s):  
Total Variation ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Energy Models ◽  
Energy Diffusion ◽  
Total Variation Model ◽  
Local Coordinates ◽  
Diffusion Speed ◽  
Adaptive Total Variation

In this paper, we introduce two novel total variation models to deal with speckle noise in ultrasound image in order to retain the fine details more effectively and to improve the speed of energy diffusion during the process. Firstly, two new convex functions are introduced as regularization term in the adaptive total variation model, and then, the diffusion performances of Hypersurface Total Variation (HYPTV) model and Logarithmic Total Variation (LOGTV) model are analyzed mathematically through the physical characteristics of local coordinates. We have shown that the larger positive parameter in the model is set, the greater energy diffusion speed appears to be, but it will cause the image to be too smooth that required adequate attention. Numerical experimental results show that our proposed LOGTV model for speckle noise removal is superior to traditional models, not only in visual effect but also in quantitative measures.

Speckle noise removal in ultrasound images by first- and second-order total variation

2017 ◽  
Vol 78 (2) ◽  
pp. 513-533 ◽  
Author(s):  
Si Wang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Jin-Jin Mei ◽  
Jie Huang
Keyword(s):  
Total Variation ◽  
Speckle Noise ◽  
Noise Removal ◽  
Second Order ◽  
Ultrasound Images

scholarly journals Performance Comparison of SAR Image Speckle Noise Removal Algorithms

2021 ◽  
Vol 183 (18) ◽  
pp. 14-19
Author(s):  
Yonatan Nagesa ◽  
S. Nagarajan ◽  
Fikiru Negesa
Keyword(s):  
Speckle Noise ◽  
Noise Removal ◽  
Performance Comparison ◽  
Sar Image

scholarly journals A fuzzy edge detector driven telegraph total variation model for image despeckling

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Sudeb Majee ◽  
Subit K. Jain ◽  
Rajendra K. Ray ◽  
Ananta K. Majee
Keyword(s):  
Total Variation ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Speckle Noise ◽  
Noise Removal ◽  
Present System ◽  
Edge Detector ◽  
Variation Equation ◽  
Image Despeckling

<p style='text-indent:20px;'>Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.</p>

scholarly journals Speckle Noise Reduction via Nonconvex High Total Variation Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yulian Wu ◽  
Xiangchu Feng
Keyword(s):  
Total Variation ◽  
Optimization Problem ◽  
Speckle Noise ◽  
Noise Removal ◽  
Excellent Performance ◽  
Variation Approach ◽  
Staircase Effect ◽  
Generalized Variation ◽  
Speckle Noise Reduction ◽  
Image Details

We address the problem of speckle noise removal. The classical total variation is extensively used in this field to solve such problem, but this method suffers from the staircase-like artifacts and the loss of image details. In order to resolve these problems, a nonconvex total generalized variation (TGV) regularization is used to preserve both edges and details of the images. The TGV regularization which is able to remove the staircase effect has strong theoretical guarantee by means of its high order smooth feature. Our method combines the merits of both the TGV method and the nonconvex variational method and avoids their main drawbacks. Furthermore, we develop an efficient algorithm for solving the nonconvex TGV-based optimization problem. We experimentally demonstrate the excellent performance of the technique, both visually and quantitatively.

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