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Novel super-resolution algorithms and enhanced noise removal algorithm for image restoration systems and applications.

机译:适用于图像恢复系统和应用的新颖超分辨率算法和增强型降噪算法。

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摘要

This dissertation is concerned with the introduction of a systematic way of modeling image processing. A dynamic imaging system model constructed from an information theory framework is proposed. Unlike an earlier simple model, the proposed dynamic imaging system (DIS) model is suitable for a wide range of applications. This DIS model is inspired by the Shannon communication theory. The Shannon communication theory is credited for the rapid development of the communication industry. Currently, most image processing researchers focus on developing fast algorithms and better hardware. An information theoretic-based approach to image processing could bring as large an impact to the image processing area as Shannon's communication theory had on the communications area.; This proposed DIS model will use the information obtained from the acquired images to provide an estimation of the unknown atmospheric turbulence, vibration, etc. It will also automatically adjust the sampling rate, wavelength band, and algorithms of choice, to produce the best possible restored image with limited information under uncertainty.; This dissertation develops the concept of the DIS model including its basic components. We have implemented three parts of this system. First, we implemented a noise removal algorithm based on the Markov random field (MRF). It is shown that this algorithm achieves better performance than other MRF-based algorithms in noise removal. Second, we have implemented a hybrid maximum likelihood/projection-on-convex-set image restoration algorithm and demonstrate that it outperforms the maximum likelihood algorithm. Third, we have implemented a self-organized map-based image restoration algorithm and compare its performance to several well-known methods. It can be implemented in parallel processing to achieve super-resolution in real time without performing a time consuming iteration process. The impact of the development of these DIS system critical components is discussed and future research areas are elucidated.
机译:本文的目的是介绍一种系统的图像处理建模方法。提出了一种基于信息论框架的动态成像系统模型。与早期的简单模型不同,所提出的动态成像系统(DIS)模型适用于广泛的应用。这种DIS模型是受Shannon传播理论启发的。香农通信理论因通信行业的快速发展而著称。当前,大多数图像处理研究人员致力于开发快速算法和更好的硬件。基于信息论的图像处理方法可能会对图像处理领域产生巨大的影响,就像香农的传播理论对通信领域产生的影响一样。提议的DIS模型将使用从获取的图像中获得的信息来估计未知的大气湍流,振动等。还将自动调整采样率,波段和选择的算法,以产生最佳的恢复效果。在不确定情况下信息有限的图像;本文提出了DIS模型的概念及其基本组成部分。我们已经实现了该系统的三个部分。首先,我们基于Markov随机场(MRF)实现了噪声消除算法。结果表明,该算法在除噪方面比其他基于MRF的算法具有更好的性能。其次,我们实现了混合最大似然/凸集投影图像复原算法,并证明它优于最大似然算法。第三,我们实现了一种自组织的基于地图的图像恢复算法,并将其性能与几种知名方法进行了比较。它可以在并行处理中实现,以实时实现超分辨率,而无需执行耗时的迭代过程。讨论了这些DIS系统关键组件开发的影响,并阐明了未来的研究领域。

著录项

  • 作者

    Pang, Ho-Yuen.;

  • 作者单位

    The University of Arizona.;

  • 授予单位 The University of Arizona.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 220 p.
  • 总页数 220
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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