Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space

碩士 === 中原大學 === 機械工程研究所 === 103 === Some traditional methods for image contrast enhancement are based on histogram equalization, which however has the drawbacks of productions of visual artifacts or excessive image strengthening due to human adjustment parameters. This thesis proposed a novel method...

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Main Authors: Boting-Rex Lin, 林柏廷
Other Authors: Po-Ting Lin
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/pd4p7n
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spelling ndltd-TW-103CYCU54890262019-05-15T22:07:30Z http://ndltd.ncl.edu.tw/handle/pd4p7n Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space 基於在CIELAB 色彩空間中進行行模糊C 均值聚類類的模糊自動化對比度度增強法的研發 Boting-Rex Lin 林柏廷 碩士 中原大學 機械工程研究所 103 Some traditional methods for image contrast enhancement are based on histogram equalization, which however has the drawbacks of productions of visual artifacts or excessive image strengthening due to human adjustment parameters. This thesis proposed a novel method called Fuzzy Automatic Contrast Enhancement (FACE). FACE first performs a fuzzy clustering method to segment an image while the pixels with similar colors in the CIELAB color space are classified into smaller image clusters with similar characteristics. The pixels in each group are then to be spread out away from the center of the belonging cluster in the RBD color space in order to enhance the image contrast but keeping the similarity of pixel colors in the same cluster. A universal contrast enhancement variable ( UCEV) was defined and optimized to maximize the image randomness (i.e. entropy of the image) in order to automatically enhance the image contrast. A more uncongested pixel distribution of the image ensures a greater image contrast. The proposed entropy-maximization method is capable of improving the image quality without man-made control parameters. The fully automated image enhancement process intelligently clusters the pixels with similar color characteristics and is general for the contrast enhancement of images in various color distributions. In this thesis, many images with different color distributions were tested experimentally and the results showed that FACE is capable of avoiding visual artifacts and excessive strengthening. Compared with the traditional histogram equalization method, the proposed method shows higher effectiveness in contrast enhancement and performs better in retaining the colors of the original images. Po-Ting Lin 林柏廷 2015 學位論文 ; thesis 114 zh-TW
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language zh-TW
format Others
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description 碩士 === 中原大學 === 機械工程研究所 === 103 === Some traditional methods for image contrast enhancement are based on histogram equalization, which however has the drawbacks of productions of visual artifacts or excessive image strengthening due to human adjustment parameters. This thesis proposed a novel method called Fuzzy Automatic Contrast Enhancement (FACE). FACE first performs a fuzzy clustering method to segment an image while the pixels with similar colors in the CIELAB color space are classified into smaller image clusters with similar characteristics. The pixels in each group are then to be spread out away from the center of the belonging cluster in the RBD color space in order to enhance the image contrast but keeping the similarity of pixel colors in the same cluster. A universal contrast enhancement variable ( UCEV) was defined and optimized to maximize the image randomness (i.e. entropy of the image) in order to automatically enhance the image contrast. A more uncongested pixel distribution of the image ensures a greater image contrast. The proposed entropy-maximization method is capable of improving the image quality without man-made control parameters. The fully automated image enhancement process intelligently clusters the pixels with similar color characteristics and is general for the contrast enhancement of images in various color distributions. In this thesis, many images with different color distributions were tested experimentally and the results showed that FACE is capable of avoiding visual artifacts and excessive strengthening. Compared with the traditional histogram equalization method, the proposed method shows higher effectiveness in contrast enhancement and performs better in retaining the colors of the original images.
author2 Po-Ting Lin
author_facet Po-Ting Lin
Boting-Rex Lin
林柏廷
author Boting-Rex Lin
林柏廷
spellingShingle Boting-Rex Lin
林柏廷
Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
author_sort Boting-Rex Lin
title Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
title_short Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
title_full Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
title_fullStr Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
title_full_unstemmed Development of Fuzzy Automatic Contrast Enhancement Based onFuzzy C-Means Clustering in CIELAB Color Space
title_sort development of fuzzy automatic contrast enhancement based onfuzzy c-means clustering in cielab color space
publishDate 2015
url http://ndltd.ncl.edu.tw/handle/pd4p7n
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