scholarly journals A Review on Image Contrast Enhancement Techniques

2019 ◽  
Vol 5 (7) ◽  
pp. 5
Author(s):  
Pooja Patel ◽  
Arpana Bhandari
Keyword(s):  
Image Restoration ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Research Work ◽  
Image Features ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Restoration Techniques ◽  
Degraded Image

The purpose of image enhancement and image restoration techniques is to perk up a quality and feature of an image that result in improved image than the original one. Unlike the image restoration, image enhancement is the modification of an image to alter impact on the viewer. Generally enhancement distorts the original digital values; therefore enhancement is not done until the restoration processes are completed. In image enhancement the image features are extracted instead of restoration of degraded image. Image enhancement is the process in which the degraded image is handled and the appearance of the image by visual is improved. It is a subjective process and increases contrast of image but image restoration is a more objective process than image enhancement. Many research work have been done for image enhancement. In this paper, different techniques and algorithms are discussed for contrast enhancement.

scholarly journals An Efficient Image Contrast Enhancement Method using Sigmoid Function and Differential Evolution

2020 ◽  
Vol 4 (3) ◽  
pp. 162
Author(s):  
Kim-Ngan Nguyen-Thi ◽  
Ha Che-Ngoc ◽  
Anh-Thy Pham-Chau
Keyword(s):  
Differential Evolution ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Image Features ◽  
Image Contrast ◽  
Sigmoid Function ◽  
Creative Commons ◽  
De Algorithm ◽  
Image Contrast Enhancement ◽  
Enhancement Method

Image enhancement is an adjusting process to make an image more appropriate for certain applications. The contrast enhancement is one of the most frequently used image enhancement methods. In this study, we introduce a new image contrast enhancement method using a link between sigmoid function and Differential Evolution (DE) algorithm. DE algorithm is performed to identify the parameters in sigmoid function so that they can maximize the measure of contrast. The experimental results show that the proposed method not only retains the original image features but also enhances the contrast effectively. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

scholarly journals Image contrast enhancement techniques-a survey

2018 ◽  
Vol 7 (3.3) ◽  
pp. 466 ◽  
Author(s):  
V S. Padmavathy ◽  
Dr R. Priya
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Research Study ◽  
Research Work ◽  
Image Contrast ◽  
Major Goal ◽  
Image Contrast Enhancement ◽  
The Comparative Study ◽  
Visual Difference

Image Enhancement plays an essential role in a wide area of vision applications. Image enhancement is a technique used to enhance the qual-ity of the image such that it can be easily viewed by both men and machine.Contrast makes a visual difference that makes an object distin-guishable from background and other objects. The major goal of image contrast enhancement is to increase the visual quality of the image. In this research study, various image contrast enhancement techniques are reviewed. This research work also focuses on the comparative study of contrast enhancement techniques for identifying an effective contrast enhancement technique.  

Comparison of the performance of three domain transform filters for radiographic contrast enhancement of welded objects

2020 ◽  
Vol 62 (6) ◽  
pp. 352-356
Author(s):  
E Yahaghi ◽  
M E Hosseini-Ashrafi
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Image Features ◽  
Image Contrast ◽  
X Rays ◽  
Radiographic Images ◽  
Recursive Filtering ◽  
Image Contrast Enhancement ◽  
Image Detail

Weld quality inspection using industrial radiography is considered to be one of the most important processes in critical industries such as aeronautical manufacturing. The quality of radiographic images of welded industrial parts may suffer from poor signal-to-noise ratio (SNR), the main cause of which is the unavoidable detection of scattered X-rays. Image processing methods may be used to enhance image contrast and achieve improved defect detection. In this study, the outcomes from three different image contrast enhancement spatial domain transform algorithms are analysed and compared. The three algorithms used are normalised convolution (NC), interpolated convolution (IC) and recursive filtering (RF). Based on the results of qualitative operator perception, the study shows that the application of all three methods results in improved image contrast, enabling enhanced visualisation of image detail. Subtle differences in performance between the outputs from the different algorithms are noted, especially around the edges of image features. Furthermore, it is found that RF is approximately two orders of magnitude quicker than the other algorithms, making it more suitable for online weld inspection lines.

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 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)

scholarly journals Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method

2021 ◽  
Vol 26 (1) ◽  
pp. 95-101
Author(s):  
Gutta Srinivasa Rao ◽  
Atluri Srikrishna
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Satellite Image ◽  
Histogram Equalization ◽  
Image Contrast ◽  
The Poor ◽  
Image Contrast Enhancement ◽  
Poor Lighting ◽  
Equalization Method ◽  
Image Datasets

Image Enhancement methods produce various sorts of problems, for example, unnatural impacts, over-improvement, and these downsides become increasingly unmistakable in improving dull Images. Histogram Equalization (HE) method is a straightforward and generally utilized Image contrast enhancement procedure. The fundamental task of HE is it changes the contrast of the Image. To perform this task, different HE techniques have been proposed. These techniques protect the brightness or contrast on the final Image that doesn't have a characteristic look. To overcome the drawbacks of HE, Enhanced Multi Histogram Equalization (EMHE) technique is proposed, which divide the Image into a few sub images and again these sub images are divided into sub-images, and traditional HE strategy is applied to each sub Image for getting better results. The improvement is brought about by repetitive data present in sub-pixel moves between relating Lightroom (LR) Images of a similar scene. The principal phase of the development incorporates Image enrollment of LR Images utilizing known parameters and geo-referencing methods for manufactured and genuine information individually. The proposed development of M-HE Images has been assessed on the LR Images obtained from satellite Image datasets to exhibit the clarity of the images by enhancing the contrast on the poor lighting images.

Sign in / Sign up

Export Citation Format

Share Document