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An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement

An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement

Shantharajah S. P., Ramkumar T, Balakrishnan N
Copyright: © 2017 |Pages: 12
ISBN13: 9781522520535|ISBN10: 1522520538|EISBN13: 9781522520542
DOI: 10.4018/978-1-5225-2053-5.ch005
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MLA

S. P., Shantharajah, et al. "An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement." Advanced Image Processing Techniques and Applications, edited by N. Suresh Kumar, et al., IGI Global, 2017, pp. 121-132. https://doi.org/10.4018/978-1-5225-2053-5.ch005

APA

S. P., S., T, R., & N, B. (2017). An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement. In N. Kumar, A. Sangaiah, M. Arun, & S. Anand (Eds.), Advanced Image Processing Techniques and Applications (pp. 121-132). IGI Global. https://doi.org/10.4018/978-1-5225-2053-5.ch005

Chicago

S. P., Shantharajah, Ramkumar T, and Balakrishnan N. "An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement." In Advanced Image Processing Techniques and Applications, edited by N. Suresh Kumar, et al., 121-132. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2053-5.ch005

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Abstract

Image enhancement is a quantifying criterion for sharpening and enhancing image quality, where many techniques are empirical with interactive procedures to obtain précised results. The proposed Intensity Histogram Equalization (IHE) approach conquers the noise defects that has a preprocessor to remove noise and enhances image contrast, providing ways to improve the intensity of the image. The preprocessor has the mask production, enlightenment equalization and color normalization for efficient processing of the images which generates a binary image by labeling pixels, overcomes the non-uniform illumination of image and classifies color capacity, respectively. The distinct and discrete mapping function calculates the histogram values and improves the contrast of the image. The performance of IHE is based on noise removal ratio, reliability rate, false positive error measure, Max-Flow Computational Complexity Measure with NDRA and Variation HOD. As the outcome, the different levels of contrast have been significantly improved when evaluated against with the existing systems.

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