Image de-noising and enhancement based on rough set and principal component analysis

机译：基于粗糙集和主成分分析的图像去噪与增强

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In this paper, we conduct research on novel image de-noising and enhancement algorithm based on rough set theory and the principal component analysis. Mathematically, image de-noising belongs to ill-posed inverse problem an effective way to solve the discomfort of basic qualitative is introducing a priori information about the image in image processing as the image de-noising is transformed into the well-posed problem. Under this guidance, we propose the novel perspective on the set theory and the principal component analysis based methodology. In the future research, we will integrate the experimental analysis for further optimization.

机译：在本文中，我们基于粗糙集理论和主成分分析对新型图像降噪和增强算法进行了研究。从数学上讲，图像降噪属于不适定反问题，一种解决基本定性不适的有效方法是在图像处理中引入关于图像的先验信息，将图像降噪转化为适定问题。在此指导下，我们提出了关于集合论和基于主成分分析的方法论的新颖观点。在未来的研究中，我们将整合实验分析以进一步优化。

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来源
《2017 International Conference on Inventive Computing and Informatics》|2017年|536-539|共4页
会议地点 Coimbatore(IN)
作者
Wenzhun Huang; Harry Haoxiang Wang; Zhe Liu; Liping Wang;
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作者单位

School of Information Engineering, Xijing University, Xi'an, China;

GoPerception Laboratory, NY, USA;

School of Information Engineering, Xijing University, Xi'an, China;

School of Information Engineering, Xijing University, Xi'an, China;

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原文格式 PDF
正文语种 eng
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关键词
Principal component analysis; Rough sets; Image denoising; Noise reduction; Hyperspectral imaging; Filtering theory; Maximum likelihood detection;

机译：主成分分析粗糙集图像去噪降噪高光谱成像滤波理论最大似然检测;

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2. Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory [J] . He Yongxiu, Pang Yuexia, Zhang Qi, Renewable energy . 2018,第JULa期

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3. Knowledge acquisition based on rough set theory and principal component analysis [J] . An Zeng, Dan Pan, Qi-Lun Zheng, IEEE intelligent systems . 2006,第2期

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4. Image de-noising and enhancement based on rough set and principal component analysis [C] . Wenzhun Huang, Harry Haoxiang Wang, Zhe Liu, International Conference on Inventive Computing and Informatics . 2017

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5. Image correspondence feature sets based on principal components. [D] . Pineda Torres, Ivo Humberto. 2002

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6. Efficient principal component analysis for multivariate 3D voxel‐based mapping of brain functional imaging data sets as applied to FDG‐PET and normal aging [O] . Gerhard Zuendorf, Nacer Kerrouche, Karl Herholz, 2003

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7. Adaptive Principal Component Analysis Based Wavelet Transform and Image De-noising for Face Recognition Applications [O] . Isra’a Abdul-ameer Abdul-jabbar, Jieqing Tan, Zhengfeng Hou 2015

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Image de-noising and enhancement based on rough set and principal component analysis

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