scholarly journals Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
P. Jagatheeswari ◽  
S. Suresh Kumar ◽  
M. Mary Linda
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Dynamic Range ◽  
Median Filter ◽  
Histogram Equalization ◽  
Input Image ◽  
Enhancement Technique ◽  
Image Contrast Enhancement ◽  
The Mean ◽  
Plateau Limit

The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

scholarly journals An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

2010 ◽  
Vol 3 (1) ◽  
pp. 43 ◽  
Author(s):  
M. A. Yousuf ◽  
M. R. H. Rakib
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Input Image ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Calculated Probability ◽  
The Mean ◽  
Processing Techniques

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0.  Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299                J. Sci. Res. 3 (1), 43-50 (2011)

Sequential Model for Digital Image Contrast Enhancement

2020 ◽  
Vol 13 ◽  
Author(s):  
Monika Agarwal ◽  
Geeta Rani ◽  
Shilpy Agarwal ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Contrast Enhancement ◽  
Maximum Entropy ◽  
Histogram Equalization ◽  
Input Image ◽  
Maximum Brightness ◽  
Low Contrast ◽  
Entropy Maximization ◽  
Image Contrast Enhancement ◽  
Constrained Model ◽  
Minimum Noise

Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.

scholarly journals Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images.

2021 ◽  
Vol 20 (1) ◽  
pp. 84-98
Author(s):  
FAHMI AKMAL DZULKIFLI
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Enhancement Technique ◽  
Brightness Preservation ◽  
Microscopic Images ◽  
Histopathological Images ◽  
Contrast Stretching

Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images. 

scholarly journals Implementation of Modified Particle Swarm Optimization Algorithm for Image Contrast Enhancement

2020 ◽  
Vol 9 (4) ◽  
pp. 1030-1036
Keyword(s):  
Image Processing ◽  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Overall Evaluation

Image contrast enhancement is mostly preferred approach for Medical Image Processing field. In this work, a new Histogram Specification (HS) based image contrast enhancement scheme is described. The optimal values of the weighing constraints are progressed through the standard of Modified Particle Swarm Optimization (MPSO) with respect to the histogram of the input processed images. The proposed scheme improves the contrast of the input processed image superior than its existing histogram equalization methods. Henceforth, this method can efficiently be utilized in the fields including image processing, electronics etc. The overall evaluation of the HS scheme can be computed by means of Discrete Entropy (DE) and Contrast Improvement Index (CII).

scholarly journals Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

scholarly journals Fast contrast enhancement by adaptive pixel value stretching

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879380
Author(s):  
Gang Cao ◽  
Huawei Tian ◽  
Lifang Yu ◽  
Xianglin Huang
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Computational Cost ◽  
Computer Applications ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Image Contrast Enhancement ◽  
Quantitative Performance ◽  
Pixel Value ◽  
Adaptive Gamma Correction

In this article, we propose a fast and effective method for digital image contrast enhancement. The gray-level dynamic range of contrast-distorted images is extended maximally via adaptive pixel value stretching. The quantity of saturated pixels is set intelligently according to the perceptual brightness of global images. Adaptive gamma correction is also novelly used to recover the normal luminance in enhancing dimmed images. Different from prior methods, our proposed technique could be enforced automatically without complex manual parameter adjustment per image. Both qualitative and quantitative performance evaluation results show that, comparing with some recent influential contrast enhancement techniques, our proposed method achieves comparative or better enhancement quality at a surprisingly lower computational cost. Besides general computer applications, such merit should also be valuable in low-power scenarios, such as the imaging pipelines used in small mobile terminals and visual sensor network.

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