scholarly journals Image Enhancement using Recursive Separate Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

2020 ◽  
Vol 9 (3) ◽  
pp. 782-787
Keyword(s):  
Threshold Value ◽  
Histogram Equalization ◽  
Specific Region ◽  
Recursive Algorithms ◽  
Image Histogram ◽  
Standard Intensity ◽  
Equalization Algorithm ◽  
Low Exposure ◽  
Average Information ◽  
Mammogram Images

To improve image contrast, this paper introduces a recursive separate standard intensity deviation based clipped sub image histogram equalization method. This is an extension of standard intensity deviation value based sub image histogram equalization algorithm, in terms of histogram separation and equalization. In existing equalization methods do not effectively utilizes the information from different region in equalization process. In this scheme, the image histogram is bisected based on standard intensity deviation value. The further separation is carried out based on the specific region threshold value and the resulting four sub histograms are equalized individually. This is an effective method for enhancing, low exposure, medical and mammogram images and for addressing the over-enhancement problem. The performance evaluation of the proposed method is presented with the help of average information and visual quality assessment and the proposed algorithm outperforms existing recursive algorithms based on histogram equalization.

scholarly journals Image Enhancement using Recursive Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

2019 ◽  
Vol 9 (2) ◽  
pp. 3933-3937
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Light Conditions ◽  
Low Light ◽  
Image Histogram ◽  
Output Image ◽  
Standard Intensity ◽  
Underwater Image ◽  
Image Inspection ◽  
Low Exposure

The low exposure image enhancement has become indispensable inimage processing for better visibility. The most challenging in image enhancement is especially to curtail overenhancement problems. This paper presents a method, performs the separation of the histogram based on respective standard intensity deviation value and then recursively equalizes all sub histograms independently. The over-enhancement problem is minimized by this method. It applies more in an underwater image, because of its low light conditions. The experiment results are analyzed in terms of entropy and output image inspection. The proposed method results show significant improvement over earlier recursive based histogram equalization algorithms.

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.

An Enhancement of Multi Classifiers Voting Method for Mammogram Image based on Image Histogram Equalization

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
◽  
Ali Ahmed ◽  
Anik Hanifatul Azizah ◽  
Saima Anwar Lashar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Image Histogram ◽  
Mammogram Image ◽  
Voting Method

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm

2017 ◽  
Vol 10 (6) ◽  
pp. 726-736
Author(s):  
许轰烈 XU Hong-lie ◽  
匡 程 KUANG Cheng ◽  
张 乐 ZHANG Le ◽  
李 莎 LI Sha ◽  
王树军 WANG Shu-jun ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Equalization Algorithm

Contrast Enhancement Using Optimum Threshold Selection

2020 ◽  
Vol 8 (3) ◽  
pp. 96-118
Author(s):  
Geeta Rani ◽  
Monika Agarwal
Keyword(s):  
Contrast Enhancement ◽  
Maximum Entropy ◽  
Dynamic Range ◽  
Threshold Value ◽  
Histogram Equalization ◽  
Threshold Method ◽  
Essential Information ◽  
Low Contrast ◽  
Brightness Preservation ◽  
Double Threshold

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

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