scholarly journals Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
P. Babu ◽  
V. Rajamani ◽  
K. Balasubramanian
Keyword(s):  
Contrast Enhancement ◽  
Mean Value ◽  
Input Image ◽  
Natural Image ◽  
Bicriteria Optimization ◽  
Histogram Modification ◽  
Multiple Peaks ◽  
Local Maxima ◽  
Novel Approach ◽  
Image Contrast Enhancement

A novel approach, Multipeak mean based optimized histogram modification framework (MMOHM) is introduced for the purpose of enhancing the contrast as well as preserving essential details for any given gray scale and colour images. The basic idea of this technique is the calculation of multiple peaks (local maxima) from the original histogram. The mean value of multiple peaks is computed and the input image’s histogram is segmented into two subhistograms based on this multipeak mean (mmean) value. Then, a bicriteria optimization problem is formulated and the subhistograms are modified by selecting optimal contrast enhancement parameters. While formulating the enhancement parameters, particle swarm optimization is employed to find optimal values of them. Finally, the union of the modified subhistograms produces a contrast enhanced and details preserved output image. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy, natural image quality evaluator, and absolute mean brightness error.

scholarly journals A Framework for Cross-Modality Guided Contrast Enhancement of CT Liver Using MRI

2021 ◽  
Vol 38 (6) ◽  
pp. 1671-1675
Author(s):  
Ahmed Elaraby ◽  
Ayman Taha
Keyword(s):  
Medical Imaging ◽  
Contrast Enhancement ◽  
Local Contrast ◽  
Ct Image ◽  
Histogram Modification ◽  
Low Contrast ◽  
Computed Tomographic ◽  
Novel Approach ◽  
Image Contrast Enhancement ◽  
Adaptive Gamma Correction

In liver medical imaging, physicians always detect, monitor, and characterize liver diseases by visually assessing of liver medical images. Computed Tomographic (CT) imaging is considered as one of the efficient medical imaging modalities in diagnosis of various human diseases. However, imprecise visualization and low contrast are the drawbacks that limit its utility. In this paper, a novel approach of multimodal liver image contrast enhancement is proposed. The idea behind the proposed approach is utilizing MRI scan as guide to exploit the diversity information extracted to enhance the structures in CT modal of liver. The proposed enhancement technique consists of two phases of enhancement to assess local contrast of the input images. In the first phase, the two image modalities are converted to the same range as the range of MRI and CT are different. Then, we did transformation of CT image so that its histogram matches the histogram of MRI. Second, the adaptive gamma correction-based histogram modification is utilized to get enhanced CT image. The subjective and objective experimental results indicated that the proposed scheme generates significantly enhanced liver CT.

scholarly journals Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 83332-83342 ◽  
Author(s):  
Hao-Tian Wu ◽  
Weiqi Mai ◽  
Shuyi Meng ◽  
Yiu-Ming Cheung ◽  
Shaohua Tang
Keyword(s):  
Contrast Enhancement ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Image Contrast ◽  
Two Dimensional ◽  
Histogram Modification ◽  
Image Contrast Enhancement

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 Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
V. Magudeeswaran ◽  
C. G. Ravichandran
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Result ◽  
Natural Image ◽  
Original Image ◽  
Qualitative And Quantitative ◽  
Image Contrast Enhancement ◽  
Fuzzy Histogram

Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC) and natural image quality evaluator (NIQE) index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.

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