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.

scholarly journals Image Enhancement based on Contrast Enhancement & Fuzzification Histogram Equalization and Comparison with Contrast Enhancement Techniques

2013 ◽  
Vol 7 (2) ◽  
pp. 594-599
Author(s):  
Shubhanshi Gupta ◽  
Ashutosh Gupta ◽  
Gagan Minocha
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Image Contrast Enhancement

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

scholarly journals Study on Construction of a Medical X-Ray Direct Digital Radiography System and Hybrid Preprocessing Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yong Ren ◽  
Sheng Wu ◽  
Mijian Wang ◽  
Zhongjie Cen
Keyword(s):  
Contrast Enhancement ◽  
Digital Radiography ◽  
Histogram Equalization ◽  
Image Contrast ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Radiography System ◽  
Image Contrast Enhancement ◽  
Flat Field ◽  
Enhancement Algorithm

We construct a medical X-ray direct digital radiography (DDR) system based on a CCD (charge-coupled devices) camera. For the original images captured from X-ray exposure, computer first executes image flat-field correction and image gamma correction, and then carries out image contrast enhancement. A hybrid image contrast enhancement algorithm which is based on sharp frequency localization-contourlet transform (SFL-CT) and contrast limited adaptive histogram equalization (CLAHE), is proposed and verified by the clinical DDR images. Experimental results show that, for the medical X-ray DDR images, the proposed comprehensive preprocessing algorithm can not only greatly enhance the contrast and detail information, but also improve the resolution capability of DDR system.

scholarly journals PERBAIKAN KUALITAS CITRA MAPS MENGGUNAKAN METODE CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dian Lestari Nasution
Keyword(s):  
Graphic Design ◽  
Histogram Equalization ◽  
Good Image ◽  
Image Processing Techniques ◽  
Adaptive Histogram Equalization ◽  
Image Contrast Enhancement ◽  
Image Improvement ◽  
Processing Techniques ◽  
Graphics Processing ◽  
Contrast Image

Map images that are on Google Maps can also be screenshooted, when taking image maps / maps the image quality is not optimal, this is because the ability of the computer used to take the image does not support high graphics. This map image can be used as material for the purposes of work that uses images such as graphic design, namely printing.The ability of computers that do not support large graphics processing in getting a good image is not a major problem in image improvement. Because the image that is not maximized, its color elements can be manipulated with digital image processing techniques, one of the methods for manipulating elements of image colors is the CLAHE method. The CLAHE method is applied and calculates the pixel values that exist in each RGB layer. In the field of graphic design images that have less intensity can be improved by increasing contrast. Image contrast enhancement can be done by the CLAHE method.Keywords: Maps Imagery, Contrast, CLAHE

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.

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