Wavelet domain image denoising via support vector regression

2004 ◽  
Vol 40 (23) ◽  
pp. 1479 ◽  
Author(s):  
H. Cheng ◽  
J.W. Tian ◽  
J. Liu ◽  
Q.Z. Yu
Keyword(s):  
Support Vector Regression ◽  
Image Denoising ◽  
Support Vector ◽  
Wavelet Domain

Adaptive Medical Image Denoising Using Support Vector Regression

2011 ◽  
pp. 494-502 ◽  
Author(s):  
Dinh Hoan Trinh ◽  
Marie Luong ◽  
Jean-Marie Rocchisani ◽  
Canh Duong Pham ◽  
Françoise Dibos
Keyword(s):  
Support Vector Regression ◽  
Image Denoising ◽  
Medical Image ◽  
Support Vector

scholarly journals Significance Support Vector Regression for Image Denoising

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1233
Author(s):  
Bing Sun ◽  
Xiaofeng Liu
Keyword(s):  
Support Vector Regression ◽  
Image Denoising ◽  
Solution Process ◽  
Spatial Density ◽  
Support Vector ◽  
Excellent Performance ◽  
Noise Interference ◽  
Measurement Framework ◽  
Penalty Factor ◽  
Cutoff Distance

As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual.

A Three-Domain Fuzzy Support Vector Regression for Image Denoising and Experimental Studies

2014 ◽  
Vol 44 (4) ◽  
pp. 516-525 ◽  
Author(s):  
Zhi Liu ◽  
Shuqiong Xu ◽  
C. L. Philip Chen ◽  
Yun Zhang ◽  
Xin Chen ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Image Denoising ◽  
Experimental Studies ◽  
Support Vector

Fuzzy density weight-based support vector regression for image denoising

2016 ◽  
Vol 339 ◽  
pp. 175-188 ◽  
Author(s):  
Yun Zhang ◽  
Shuqiong Xu ◽  
Kairui Chen ◽  
Zhi Liu ◽  
C.L. Philip Chen
Keyword(s):  
Support Vector Regression ◽  
Image Denoising ◽  
Support Vector
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