scholarly journals Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Behrouz Fathi-Vajargah ◽  
Maryam Gharehdaghi
Keyword(s):  
Markov Chains ◽  
Contrast Enhancement ◽  
Brain Magnetic Resonance Imaging ◽  
Image Contrast ◽  
Low Contrast ◽  
Fuzzy Expected Value ◽  
Image Contrast Enhancement ◽  
Magnetic Resonance Imaging Mri ◽  
Qualitative Performance ◽  
Fuzzy Image

This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI). The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image.

Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features

2019 ◽  
Vol 19 (04) ◽  
pp. 1950020
Author(s):  
Mitra Montazeri
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Memetic Algorithm ◽  
Image Features ◽  
Image Contrast ◽  
Consumer Electronics ◽  
Major Step ◽  
Processing Application ◽  
Low Contrast ◽  
Image Contrast Enhancement

In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.

scholarly journals An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 161584-161593 ◽  
Author(s):  
Awais Mahmood ◽  
Sajid Ali Khan ◽  
Shariq Hussain ◽  
Eslam Mohammad Almaghayreh
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Image Contrast Enhancement

DCT Coefficients Weighting (DCTCW)-Based Gray Wolf Optimization (GWO) for Brightness Preserving Image Contrast Enhancement

2021 ◽  
Author(s):  
Saorabh Kumar Mondal ◽  
Arpitam Chatterjee ◽  
Bipan Tudu
Keyword(s):  
Contrast Enhancement ◽  
Objective Evaluation ◽  
Brightness Distribution ◽  
Image Contrast ◽  
Optimization Techniques ◽  
Biological Behavior ◽  
Gray Wolf ◽  
Original Image ◽  
Image Characteristics ◽  
Image Contrast Enhancement

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.

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