scholarly journals An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping

2012 ◽  
Vol 25 ◽  
pp. 601-608 ◽  
Author(s):  
Youlian Zhu ◽  
Cheng Huang
Keyword(s):  
Histogram Equalization ◽  
Gray Level ◽  
Adaptive Histogram Equalization ◽  
Equalization Algorithm ◽  
Gray Level Mapping

ANALISIS IMPLEMENTASI CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) UNTUK DETEKSI CITRA SIDIK JARI TIRUAN

2021 ◽  
Vol 2 (1) ◽  
pp. 38-44
Author(s):  
Safira Nuraisha ◽  
Sri Handayani
Keyword(s):  
Histogram Equalization ◽  
Gray Level ◽  
Adaptive Histogram Equalization

Autentikasi biometrik dengan sidik jari paling sering digunakan untuk sistem keamanan atau autentikasi sebuah akun. Seiring dengan berkembangnya model sistem keamanan menggunakan autentikasi sidik jari, muncul masalah baru yaitu penggunaan sidik jari Penggunaan sidik jari palsu dapat dilakukan melalui scanner sidik jari yang menerima salinan dari sidik jari asli yang sering disebut dengan artificial fingerprints. Penggunaan sidik jari palsu dapat mengancam keamanan dari sebuah sistem. Permasalahan deteksi sidik jari dan identifikasi bahan yang dapat meniru karakteristik sidik jari diperburuk oleh dua hal, pertama, sensor standar tidak mampu membedakan citra dari sidik jari asli dan sidik jari replika. Kedua, seringkali tidak ada isyarat yang jelas bahwa citra tersebut berasal dari sidik jari replika atau dengan kata lain sidik jari replika yang sangat mirip dengan sidik jari asli sehingga sulit untuk dibedakan. Penelitian ini bertujuan untuk mendeteksi citra sidik jari tiruan dengan tingkat akurasi yang tinggi. Dataset yang digunakan merupakan dataset publik ATVS. Metode yang diusulkan yaitu ekstraksi fitur citra sidik jari dengan kontras GLCM (Gray Level Co-Occurance Matrix) dengan metode peningkatan kualitas citra CLAHE (Contrast Limited Adaptive Histogram Equalization). Hasil deteksi citra sidik jari menggunakan CLAHE menghasilkan akurasi yang lebih baik dibandingkan tanpa menggunakan CLAHE

scholarly journals Diabetic Retinopathy Detection using Image Processing

2019 ◽  
Vol 8 (6S) ◽  
pp. 937-941
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Adaptive Histogram Equalization ◽  
Retinal Fundus Image ◽  
Equalization Algorithm ◽  
Processing Techniques ◽  
Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

Frog and Haze Image Enhancement Using Improved Histogram Equalization Method in HIS Color Space

2014 ◽  
Vol 687-691 ◽  
pp. 3671-3674
Author(s):  
Dian Yuan Han
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Image Display ◽  
Gray Level ◽  
Mapping Algorithm ◽  
Enhancement Method ◽  
Gray Level Mapping ◽  
Weight Coefficients

This paper concerns the problem of frog and haze image enhancement. Images are often degreed due to the fog and haze condition. In this paper, an image enhancement method by using improved histogram equalization in HIS color space was put forward. Firstly, the image was transformed from RGB to HIS color space. Then the S and I components were treated with improved histogram equalization separately. When judging whether a gray level was to be merged with another, the weight coefficients with increased step were assigned to these low frequency gray levels according to their distance to the current gray level. Thus the excessive gray level merging was avoided. At the same time, a non-linear gray level mapping algorithm was proposed, which improved the contrast and brightness of the image. The experimental results show that our methods could keep the original colors and details of the image better, and they could improve the frog and haze image display effects significantly.

Improved Self-Adaptive Image Histogram Equalization Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1495-1500
Author(s):  
Na Xin Peng ◽  
Yu Qiang Chen
Keyword(s):  
Image Processing ◽  
Main Idea ◽  
Histogram Equalization ◽  
Gray Level ◽  
Image Histogram ◽  
Equalization Algorithm ◽  
Self Adaptive

Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.

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