Name
Title | ||
|---|---|---|
Root-transformation based multiplicative denoising model and its statistical analysis. |
Abstract | ||
|---|---|---|
This paper focuses on the problem of multiplicative noise removal. Based on the statistical properties of the noise distribution, a quadratic penalty term which well models the priori distribution of the mth root of the noise is introduced to the denoising scheme. The new model enjoys the merit of its unconditional convexity, and the global optimum is easily obtainable by convex optimization algorithms. Moreover, an accurate estimated root order m can better reflect the statistical characteristics of the noise, resulting in a significant promotion on the performance. To solve the proposed model efficiently, a modified alternating direction method of multipliers is introduced. In the experiments, the influence of the parameter m is explicitly discussed, the denoising performance of the proposed model is compared with several state-of-the-art variational methods. The results confirm the superiority of the proposed method over others. (C) 2017 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2017.11.048 | NEUROCOMPUTING |
Keywords | Field | DocType |
Multiplicative noise,Root transformation,Quadratic penalty,Modified alternating direction method of multipliers | Noise reduction,Applied mathematics,Convexity,Multiplicative function,Pattern recognition,Quadratic equation,Global optimum,Artificial intelligence,Convex optimization,Mathematics,Multiplicative noise,Statistical analysis | Journal |
Volume | ISSN | Citations |
275 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 30 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-ping Zhao | 1 | 0 | 0.34 |
| Xiang-Chu Feng | 2 | 989 | 40.18 |
| Xi-xi Jia | 3 | 20 | 4.70 |
| Rui-qiang He | 4 | 0 | 1.01 |
| Chen Xu | 5 | 269 | 29.36 |