摘要翻译:
最近,人们对过于完备的词典及其所能提供的稀疏表示给予了极大的关注。在各种各样的信号处理问题中,稀疏性是导致高性能的一个关键特性。修复是重建图像或视频丢失或退化部分的过程,是一个有趣的应用,可以通过组合过度完备的字典对图像进行适当的分解来处理。本文提出了一种新的分解技术,并通过图像的修复进行了研究。仿真结果验证了该方法的有效性。
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英文标题:
《A New Trend in Optimization on Multi Overcomplete Dictionary toward
Inpainting》
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作者:
SeyyedMajid Valiollahzadeh, Mohammad Nazari, Massoud Babaie-Zadeh,
Christian Jutten
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最新提交年份:
2008
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Multimedia 多媒体
分类描述:Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
Recently, great attention was intended toward overcomplete dictionaries and the sparse representations they can provide. In a wide variety of signal processing problems, sparsity serves a crucial property leading to high performance. Inpainting, the process of reconstructing lost or deteriorated parts of images or videos, is an interesting application which can be handled by suitably decomposition of an image through combination of overcomplete dictionaries. This paper addresses a novel technique of such a decomposition and investigate that through inpainting of images. Simulations are presented to demonstrate the validation of our approach.
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PDF链接:
https://arxiv.org/pdf/0812.2405