scholarly journals Contrast enhancement by multiscale adaptive histogram equalization

2001 ◽  
Author(s):  
Yinpeng Jin ◽  
Laura M. Fayad ◽  
Andrew F. Laine
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Adaptive Histogram Equalization

scholarly journals Adaptive Histogram Equalization For Automatic Contrast Enhancement Of Medical Images

1986 ◽  
Author(s):  
Stephen M. Pizer ◽  
John D. Austin ◽  
John R. Perry. ◽  
Hal D. Safrit ◽  
John B. Zimmerman
Keyword(s):  
Contrast Enhancement ◽  
Medical Images ◽  
Histogram Equalization ◽  
Adaptive Histogram Equalization

scholarly journals Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru

Gravitasi ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 24-28
Author(s):  
Nurhidayah ◽  
Bannu Abdul Samad ◽  
Bualkar Abdullah
Keyword(s):  
Ct Scan ◽  
Contrast Enhancement ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Histogram Equalization ◽  
Mean Square ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan reduksi noise dengan menggunakan metode median filter. Kinerja dari setiap metode dihitung dengan mencari nilai MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) citra. Dari nilai MSE dan PSNR yang di dapatkan diperoleh nilai MSE dan PSNR terbaik pada metode contrast limited adaptive histogram equalization dengan nilai 653,434 dB dan 245,547 dB.

scholarly journals Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shibin Wu ◽  
Shaode Yu ◽  
Yuhan Yang ◽  
Yaoqin Xie
Keyword(s):  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Multiscale Analysis ◽  
Histogram Equalization ◽  
Mammographic Images ◽  
Adaptive Histogram Equalization ◽  
Gaussian Pyramid ◽  
Low Pass ◽  
Multiscale Transform ◽  
Improvement Index

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

scholarly journals Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)

2018 ◽  
Vol 16 (37) ◽  
pp. 127-135
Author(s):  
Loay Kadom Abood
Keyword(s):  
Contrast Enhancement ◽  
Infrared Imaging ◽  
Experimental Tests ◽  
Histogram Equalization ◽  
Night Vision ◽  
Infrared Images ◽  
Adaptive Histogram Equalization ◽  
General Quality ◽  
Improvement Procedures

The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed significant improvements.

Image Contrast Enhancement by Hybrid 3SAIHT and CLAHE Algorithm

2013 ◽  
Vol 479-480 ◽  
pp. 870-877
Author(s):  
Cheng Yi Yu ◽  
Hsueh Yi Lin ◽  
Cheng Jian Lin
Keyword(s):  
Contrast Enhancement ◽  
Information Fusion ◽  
Histogram Equalization ◽  
Adaptation Level ◽  
Human Visual Perception ◽  
Edge Information ◽  
Adaptive Histogram Equalization ◽  
Image Contrast Enhancement ◽  
Quality Image ◽  
Display Quality

Human visual perception is insensitive to certain shades of gray but can distinguish among 20 to 30 shades of gray under a given adaptation level. In this paper, we propose an image fusion pipeline that generates a high vision quality image by fusing the Three-Scale Adaptive Inverse Hyperbolic Tangent (3SAIHT) and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithms to increase detail and edge information. Fusion results are clearer and better with regard to display quality and contrast enhancement.

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