正规化(语言学)
巴克斯-吉尔伯特法
最近梯度学习法
计算机科学
全变差去噪
规范(哲学)
支持向量机的正则化研究进展
算法
人工智能
计算机视觉
数学优化
数学
图像(数学)
反问题
数学分析
认识论
Tikhonov正则化
哲学
作者
Vania V. Estrela,Hermes Aguiar Magalhães,Osamu Saotome
出处
期刊:Advances in computational intelligence and robotics book series
日期:2016-01-01
卷期号:: 41-64
被引量:9
标识
DOI:10.4018/978-1-4666-8654-0.ch002
摘要
The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter ? controls the amount of regularization allowed and it is essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.
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