.png){kind=icon}
返回
A Multiplicative Iterative Algorithm for Box-Constrained Penalized Likelihood Image Restoration
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2012.2188811.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image restoration is a computationally intensive problem as a large number of pixel values have to be determined. Since the pixel values of digital images can attain only a finite number of values (e. g., 8-bit images can have only 256 gray levels), one would like to recover an image within some dynamic range. This leads to the imposition of box constraints on the pixel values. The traditional gradient projection methods for constrained optimization can be used to impose box constraints, but they may suffer from either slow convergence or repeated searching for active sets in each iteration. In this paper, we develop a new box-constrained multiplicative iterative (BCMI) algorithm for box-constrained image restoration. The BCMI algorithm just requires pixelwise updates in each iteration, and there is no need to invert any matrices. We give the convergence proof of this algorithm and apply it to total variation image restoration problems, where the observed blurry images contain Poisson, Gaussian, or salt-and-pepper noises.
Keyword:
Box constraints
box-constrained multiplicative iterative (BCMI) algorithm
global convergence
image restoration
penalized likelihood (PL) optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization通过中值型噪声检测器和细节保持正则化去除椒盐噪声
PREAI
分享收藏
查看更多内容