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Low Contrast Image Enhancement Using Optimal Sub-Image.

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Bibliographische Detailangaben
Titel: Low Contrast Image Enhancement Using Optimal Sub-Image.
Autoren: Saini, Manish Kumar, Saini, Sumit
Quelle: IUP Journal of Electrical & Electronics Engineering; Oct2018, Vol. 11 Issue 4, p7-20, 14p
Schlagwörter: IMAGE processing, IMAGING systems, SIGNAL processing, DIGITAL image processing, IMAGE quality analysis, IMAGE enhancement (Imaging systems)
Abstract: The paper proposes local information-based contrast enhancement, which exploits the local information of the image extracted from sub-images of the color image. The number of sub-images or, in other words, the level of local information is optimized using wild goat algorithm to achieve the maximum contrast enhancement. Division of image into sub-images prior to applying the enhancement technique facilitates the enhancement of small blocks to prevent underexpose or overexpose in images, which usually results when any enhancement technique is applied over the complete image uniformly. The proposed technique performs stretching of histogram of small subimages according to fuzzy logic having average intensity of sub-image as its input. The number of sub-images is optimized by wild goat algorithm with performance measures, i.e., Tenengrad and CII as the cost function. The proposed technique performs better than histogram equalization and conventional fuzzy-based methods. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The paper proposes local information-based contrast enhancement, which exploits the local information of the image extracted from sub-images of the color image. The number of sub-images or, in other words, the level of local information is optimized using wild goat algorithm to achieve the maximum contrast enhancement. Division of image into sub-images prior to applying the enhancement technique facilitates the enhancement of small blocks to prevent underexpose or overexpose in images, which usually results when any enhancement technique is applied over the complete image uniformly. The proposed technique performs stretching of histogram of small subimages according to fuzzy logic having average intensity of sub-image as its input. The number of sub-images is optimized by wild goat algorithm with performance measures, i.e., Tenengrad and CII as the cost function. The proposed technique performs better than histogram equalization and conventional fuzzy-based methods. [ABSTRACT FROM AUTHOR]
ISSN:2583519X