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 基于马尔可夫随机场和像元图的图象降噪 Alternative Title Pixon-Based Image Denosing Using MRFs 吕晴 Subtype 工学硕士 Thesis Advisor 蒋田仔 2000-06-01 Degree Grantor 中国科学院自动化研究所 Place of Conferral 中国科学院自动化研究所 Degree Discipline 模式识别与智能系统 Keyword 马尔可夫随机场 图象降噪 像元 模拟退火 Markov Random Fields Pixon Pixon Map Simulated Annealing Abstract 在图象处理领域中，图象复原问题备受关注，有效的图象复原算法在 诸多图象处理应用大有用处。在这里，我们只讨论被噪声污染的图象复 原问题，或称之为图象降噪。 马尔可夫随机场(MRF)理论是概率论的一个分支，它提供了一个方便有 效的框架，可以为特定问题的上下文约束建立数学模型。这些先验知识 提供了一个正则化框架，使得基于MRF建立的数学模型的求解过程更稳 定。自1984年Gemman and Gemman提出一个MAP MRF计算框架以来，涌 现出许多将MRF。应用于图象处理问题的研究工作。马尔可夫随机场以其 强大的建模能力，获得广泛的应用，其领域几乎涵盖了计算机视觉和图 象处理的所有领地。 我们研究的重点是图象降噪，提出的算法既要降低噪声还要尽可能保 留图象中不连续区域。论文中算法兼顾以下三个方面： ·空间依赖性(Spatial Dependency) ·不连续性(Discontinuity) ·最优图象模型(Optimal Image Model) 我们应用贝叶斯计算框架建立新的图象降噪算法，在马尔可夫模型中嵌 入多尺度图象描述模型——像元图，这一数学模型能在降低噪声的同时， 并且尽量保持图象中的有效信息。最后，我们给出大量的实验结果，并 详细分析了如何选取算法中的参数。 Other Abstract It is well known that image restoration is an essential preprocessing step for many image analysis applications. Image restoration has been widely investigated in the field of image processing. So far, the majority of works have been devoted to the image denoising. For this issue, the most common problem is that some interesting structures in the image will be removed from the concerned image during noise suppression. Such interesting structures in an image often correspond to the discontinuities of the image. In this paper, we propose a novel pixon-based multiresolution method for image denoising. The key idea to our approach is that a pixon map is embedded into the MRF models under a Bayesian framework. The remarkable advantage of our approach is that restoring corrupted images and preserving the shape transitions in the restored results have been orchestrated very well. i simulated annealing algorithm is implemented to find the MAP solution. Experiments illustrate that our method is much more effective and powerful than the Wiener and median filtering techniques, two most typical and widely used techniques. At the end of this paper, we thoroughly discussed the issues on how to select effective parameters of our algorithm. Although the work is based on experiments, our results do give some wise advice on this issue. shelfnum XWLW561 Other Identifier 561 Language 中文 Document Type 学位论文 Identifier http://ir.ia.ac.cn/handle/173211/7294 Collection 毕业生_硕士学位论文 Recommended CitationGB/T 7714 吕晴. 基于马尔可夫随机场和像元图的图象降噪[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2000.
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