Ramp Distribution-Based Contrast Enhancement Techniques and Over-Contrast Measure

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Our approach to contrast enhancement (CE) of images is based on natural scene statistics (NSS). We show, in this paper, that the average intensity distribution of natural images can be linearly approximated to the ramp distribution in an ordered histogram domain as the contrast increases. Based on this finding, we propose ramp distribution-based global and local CE algorithms. The ramp distribution-based slant thresholding (RDST) algorithm is proposed as a global CE method which uses slant thresholding in an ordered histogram domain to yield a contrast-enhanced image. Also, the ramp distributionbased adaptive slant thresholding (RDAST) algorithm is proposed as a local CE method. It adaptively adjusts a slant angle of the ramp distribution in each block to suppress noise amplification in uniform regions and maximizes contrast in non-uniform regions. The RDAST also employs a scaled global modified histogram to minimize sensitivity to block size changes. Moreover, we propose a metric to measure the amount of over-contrast in an image to evaluate all CE algorithms more correctly. The experimental results show that the proposed algorithms have better or competitive performance as well as computational efficiency.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-05
Article Type
Article
Citation

IEEE ACCESS, v.7, pp.73004 - 73019

ISSN
2169-3536
DOI
10.1109/ACCESS.2019.2920015
URI
http://hdl.handle.net/10203/263204
Appears in Collection
EE-Journal Papers(저널논문)
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