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 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.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

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.  

Real-time Image Contrast Enhancement VLSI Design for Intelligent Autonomous Vehicles

2020 ◽  
Vol 64 (1) ◽  
pp. 10504-1-10504-11
Author(s):  
Shih-Lun Chen ◽  
Chia-En Chang ◽  
Chiung-An Chen ◽  
Patricia Angela R. Abu ◽  
Ting-Lan Lin ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Autonomous Vehicles ◽  
Vlsi Design ◽  
Image Contrast ◽  
Gate Count ◽  
Image Contrast Enhancement ◽  
Block Based ◽  
Frame Processing ◽  
Time Image

Abstract A novel hardware-oriented image contrast enhancement algorithm is proposed in this study for intelligent autonomous vehicles. It utilizes a weighted filter and calculates the brightness values of an image based on the adjusted image. The brightness values are processed to either reduce or increase the brightness values of the points. To further improve the quality of an image, the algorithm implements a block-based pixel processing as opposed to a per image frame processing. The brightness values for each block or area in the image are used to improve the contrast of the image. This is accomplished by reducing or increasing the different brightness values of the pixel or lifting point in each block. Simulation results showed that compared with previously proposed algorithms, this work improved on the average discrete entropy by 1% and increased the average color enhancement factor by 8.5%. The proposed novel algorithm was realized using TSMC 0.18 μm CMOS cell process. The VLSI design has a total gate count of 6028 and operates with a frequency of 201 MHz and a power rating of 17.47 mW.

scholarly journals Image Enhancement based on Contrast Enhancement & Fuzzification Histogram Equalization and Comparison with Contrast Enhancement Techniques

2013 ◽  
Vol 7 (2) ◽  
pp. 594-599
Author(s):  
Shubhanshi Gupta ◽  
Ashutosh Gupta ◽  
Gagan Minocha
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Image Contrast Enhancement

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

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.

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