A Novel 3-D Color Histogram Equalization Method With Uniform 1-D Gray Scale Histogram

2011 ◽  
Vol 20 (2) ◽  
pp. 506-512 ◽  
Author(s):  
Ji-Hee Han ◽  
Sejung Yang ◽  
Byung-Uk Lee
Keyword(s):  
Histogram Equalization ◽  
Color Histogram ◽  
Gray Scale ◽  
Equalization Method

An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization

2013 ◽  
Vol 321-324 ◽  
pp. 1133-1137
Author(s):  
Yu Ting Song ◽  
Xiu Hua Ji ◽  
Shi Lin Zhao
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Histogram Equalization ◽  
Color Images ◽  
Color Histogram ◽  
Test Results ◽  
Gray Scale ◽  
Color Image Enhancement ◽  
Equalization Algorithm ◽  
Enhancement Algorithm

This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27091-27114
Author(s):  
Qingjie Cao ◽  
Zaifeng Shi ◽  
Rong Wang ◽  
Pumeng Wang ◽  
Suying Yao
Keyword(s):  
Histogram Equalization ◽  
Two Dimensional ◽  
Equalization Method

3-D Video Enhancemnt Based on Histogram Equalization and Edge Sharpening

2013 ◽  
Vol 333-335 ◽  
pp. 1129-1133 ◽  
Author(s):  
Yi Min Qiu ◽  
Shi Hong Chen ◽  
Yi Zhou ◽  
Ying Wang
Keyword(s):  
Stereo Vision ◽  
Three Dimensional ◽  
Subjective Assessment ◽  
Histogram Equalization ◽  
Research Field ◽  
Experimental Results ◽  
Two Dimensional ◽  
Video Mode ◽  
Equalization Method ◽  
Edge Sharpening

With the development of stereo vision, much more attention has paid from two-dimensional to three-dimensional (3-D) spaces, research on 3-D image/video becomes an inevitable trend presently. We present a novel research field that focused on the enhancement of 3-D videos, using two different 3-D videos and enhancing them with histogram equalization and edge sharpening algorithms. And we utilize the subjective assessment in the experiments. The experimental results show that the edge sharpening method has better effect than the histogram equalization method in 3-D video mode. But we also find some problems that both methods have blurred edges.

scholarly journals A Novel Filter ed Bi-Histogram Equalization Method

2015 ◽  
Vol 18 (6) ◽  
pp. 691-700 ◽  
Author(s):  
Nyamlkhagva Sengee ◽  
Heung-Kook Choi
Keyword(s):  
Histogram Equalization ◽  
Equalization Method

scholarly journals Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 7
Author(s):  
Daniel M. Wonohadidjojo
Keyword(s):  
Fuzzy Sets ◽  
Contrast Enhancement ◽  
Artificial Bee Colony ◽  
Histogram Equalization ◽  
Intuitionistic Fuzzy Sets ◽  
Local Contrast ◽  
Intuitionistic Fuzzy ◽  
Bee Colony ◽  
Local Contrast Enhancement ◽  
Equalization Method

The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.

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