.png){kind=icon}
.png){kind=icon}
Return
Nonconvex sparse regularizer based speckle noise removal
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2012.10.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper focuses on the problem of speckle noise removal. A new variational model is proposed for this task. In the model, a nonconvex regularizer rather than the classical convex total variation is used to preserve edges/details of images. The advantage of the nonconvex regularizer is pointed out in the sparse framework. In order to solve the model, a new fast iteration algorithm is designed. In the algorithm, to overcome the disadvantage of the nonconvexity of the model, both the augmented Lagrange multiplier method and the iteratively reweighted method are introduced to resolve the original nonconvex problem into several convex ones. From the algorithm, we can obtain restored images as well as edge indicator of the images. Comprehensive experiments are conducted to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for the task of speckle noise removal. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Speckle noise
Nonconvex
Sparse
Alternative iteration
Augmented Lagrange multiplier
Iteratively reweighted method
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W