scholarly journals An Image Contrast Enhancement Method Using Brightness Preseving on the Linear Approximation CDF

2004 ◽  
Vol 11B (7) ◽  
pp. 779-784
Author(s):  
Hwa-Hyun Cho ◽  
Myung-Ryul Choi
Keyword(s):  
Contrast Enhancement ◽  
Linear Approximation ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method

An image contrast enhancement method based on genetic algorithm

2010 ◽  
Vol 31 (13) ◽  
pp. 1816-1824 ◽  
Author(s):  
Sara Hashemi ◽  
Soheila Kiani ◽  
Navid Noroozi ◽  
Mohsen Ebrahimi Moghaddam
Keyword(s):  
Genetic Algorithm ◽  
Contrast Enhancement ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

Image contrast enhancement method based on display and human visual system characteristics

2019 ◽  
Vol 58 (7) ◽  
pp. 1813 ◽  
Author(s):  
Guo Chen ◽  
Li Li ◽  
Weiqi Jin ◽  
Mingcong Liu ◽  
Feng Shi
Keyword(s):  
Visual System ◽  
Contrast Enhancement ◽  
Human Visual System ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
System Characteristics

Image contrast enhancement based on the piecewise-linear approximation of CDF

1999 ◽  
Vol 45 (3) ◽  
pp. 828-834 ◽  
Author(s):  
Sang-Yeon Kim ◽  
Dongil Han ◽  
Seung-Jong Choi ◽  
Jong-Seok Park
Keyword(s):  
Contrast Enhancement ◽  
Linear Approximation ◽  
Piecewise Linear ◽  
Image Contrast ◽  
Piecewise Linear Approximation ◽  
Image Contrast Enhancement

New adaptive image contrast enhancement method

1993 ◽  
Author(s):  
Tian-Hu Yu ◽  
Sanjit K. Mitra
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method

scholarly journals An Improved Infrared Image Contrast Enhancement Method

2019 ◽  
Vol 1302 ◽  
pp. 042019
Author(s):  
Yuanbin Wang ◽  
Jian Zhang
Keyword(s):  
Contrast Enhancement ◽  
Infrared Image ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method

scholarly journals An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

2010 ◽  
Vol 3 (1) ◽  
pp. 43 ◽  
Author(s):  
M. A. Yousuf ◽  
M. R. H. Rakib
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Input Image ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Calculated Probability ◽  
The Mean ◽  
Processing Techniques

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0.  Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299                J. Sci. Res. 3 (1), 43-50 (2011)

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