scholarly journals Lab Image and Field Image Contrast Quality Differentiation

2020 ◽  
Vol 9 (3) ◽  
pp. 3428-3431
Keyword(s):  
Image Quality ◽  
System Performance ◽  
Input Image ◽  
Image Size ◽  
Standard Size ◽  
Low Contrast ◽  
Quality Differentiation ◽  
Image Dimension ◽  
Processing Material

Image processing performance can be improved with the process of resizing the original input image to one standard size. Most of the previous studies used a standard size of 256 x 256 to provide the image as the image pre-processing material. The result of different image size dimension are shows in this research to proven that image resizing is important. Reducing image dimension size can help to improve system performance. At the same time, it is importance to keep the image quality. This study shows that by reducing image dimension, it can improve the computer or system performance more than 95%. Image quality can be measured to get helpful information for the study after resizing the image into the same standard size. In this study, measurement of contrast levels was taken to compare the quality differences between image labs and field images. It turns out that the quality of lab image produces high-quality images with good brightness over image field image.The best quality image is the images that have low contrast. Therefore in this research paper we used CLAHE method to enhance the contrast and brightness for field image.

How Good is Too Good? A Subjective Study on Over Enhancement of Images

2021 ◽  
Vol 2021 (29) ◽  
pp. 83-88
Author(s):  
Sahar Azimian ◽  
Farah Torkamani Azar ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Contrast ◽  
Current Image ◽  
Low Contrast ◽  
Image Dataset ◽  
Long Time ◽  
Reference Images ◽  
Subjective Study ◽  
Contrast Image

For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.

scholarly journals Numerical evaluation of image parameters of ETR-1

2016 ◽  
Vol 2 (1) ◽  
pp. 489-491
Author(s):  
Shamim Ahmed ◽  
Marian Krüger ◽  
Christian Willomitzer ◽  
Golam A. Zakaria
Keyword(s):  
Image Quality ◽  
Treatment Planning ◽  
Numerical Evaluation ◽  
Numerical Estimation ◽  
Low Contrast ◽  
Precise Result ◽  
Test Tool ◽  
Naked Eye ◽  
Pre Treatment

AbstractIn this work, we developed a method to handle the image quality test-tool precisely. This test-tool is important to evaluate the quality of the medical images for pre-treatment planning phase. But the achieved images are estimated by naked eyes, which does not provide the precise result. Our main goal is to get the desired image parameters numerically. This numerical estimation overcomes the limitation of naked eye observation. Hence, it enhances the pre-treatment planning. The ETR-1 test-tool is considered here. The contrast, the low contrast details and line-pairs (lp/mm) were estimated.

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

IJOSTHE ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Input Image ◽  
Sigmoid Function ◽  
Single Image ◽  
Low Light ◽  
Lighting Condition ◽  
Low Contrast ◽  
General Terms ◽  
Degraded Image ◽  
Result Analysis

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.

scholarly journals Comparison of image quality of an abdominal acquisition mode of angiography systems from four major manufacturers

2018 ◽  
Vol 5 (2) ◽  
pp. 6
Author(s):  
Lucie Sukupova ◽  
Jan Rydlo ◽  
Ondrej Hlavacek ◽  
Daniel Vedlich ◽  
Jan H. Peregrin
Keyword(s):  
Image Quality ◽  
Line Pair ◽  
Obese Patients ◽  
Clinical Images ◽  
Contrast Resolution ◽  
Low Contrast ◽  
System A ◽  
Acquisition Mode ◽  
Air Kerma Rate

Objective: The aim of this study was to compare image quality of different abdominal acquisition modes under conditions simulating obese patients whose images suffer more from noise and scatter radiation. Images were acquired in clinically used acquisition modes on the static and dynamic phantom for four angiography systems.Methods: A LEGO cart with 34 cm of PMMA and Pro-RTG Fluo18 phantom were used to simulate obese patients. The low-contrast resolution was assessed subjectively by two readers and objectively using signal-difference-to-noise ratio (SDNR) and using SDNR to air kerma rate. The line-pair resolution was assessed using the transmitted contrast value for line-pair groups.Results: Systems use different exposure parameters and dose but they differ in postprocessing too. Qualitative and quantitative assessments of noise produced similar results, images produced by systems A and C were noisier than by systems B and D. Highest SDNR was provided by System B, whilst System A produced the lowest values, which were almost the same for objects with different contrast. The image quality was affected mainly by frame lengths and postprocessing, but also by the dose. The images of the static phantom were better compared to the images of the dynamic phantom, which was an expected result.Conclusions: It was possible to identify image quality differences and to characterize features of postprocessing from measurements on standardized objects. A potential for optimization on some systems was identified, although further work, including assessment of clinical images, would be needed as part of the optimization process.

scholarly journals Computational Efficient Method for Denoising of Salt and Pepper Noise for Image Enhancement

2020 ◽  
Vol 8 (6) ◽  
pp. 538-543
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Noise Removal ◽  
Input Image ◽  
Filter Method ◽  
Pixel Intensity ◽  
Feature Information ◽  
Salt And Pepper

Digital Image processing is basically a computer-algorithm which is used to enhance the quality of image to understand the feature of image and exact the meaningful features information from image. Image processing has wider range of algorithms to be applied to the input image and can escape the difficulty as the signal distortion and add noise in input image at the time of processing of images. Noises affect the image visualization and degraded the image quality, sometimes chaotic variation in value of pixel intensity, lighting effect or because of poor contrast, image can’t be used directly because many time interest feature information not received as output that’s one reason image processing is significant for removal of noise from images, so noise removal is becomes trending field in image processing. Median filter method is one of most popular method to eradicate the effect of noise from image and it enhances the image quality to take meaningful feature easily from image. In this paper removing of noise using median filter to enhance the image quality is discussed, also the importance and applications of enhancement technique are covered. Parameter PSNR and MSE is also used to analysis the image quality along with the visualization of image. Simulation results show that Median filter gives good outcome for salt & pepper noise as compare to other filtering method. MATLAB software is used as simulation tool.

Diagnostic performance and image quality of low-tube voltage and low-contrast medium dose protocol with hybrid iterative reconstruction for hepatic dynamic CT

2021 ◽  
Author(s):  
Shintaro Ichikawa ◽  
Utaroh Motosugi ◽  
Tatsuya Shimizu ◽  
Marie Luise Kromrey ◽  
Yoshihito Aikawa ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Diagnostic Performance ◽  
Dynamic Ct ◽  
Diagnostic Ability ◽  
Tube Voltage ◽  
Low Contrast ◽  
Lesion Detectability ◽  
Hybrid Iterative Reconstruction

Objective: To evaluate the diagnostic performance and image quality of the low-tube voltage and low-contrast medium dose protocol for hepatic dynamic CT. Methods: This retrospective study was conducted between January and May 2018. All patients underwent hepatic dynamic CT using one of the two protocols: tube voltage, 80 kVp and contrast dose, 370   mgI/kg with hybrid iterative reconstruction or tube voltage, 120 kVp and contrast dose, 600  mgI/kg with filtered back projection. Two radiologists independently scored lesion conspicuity and image quality. Another radiologist measured the CT numbers of abdominal organs, muscles, and hepatocellular carcinoma (HCC) in each phase. Lesion detectability, HCC diagnostic ability, and image quality of the arterial phase were compared between the two protocols using the non-inferiority test. CT numbers and HCC-to-liver contrast were compared between the protocols using the Mann–Whitney U test. Results: 424 patients (70.5 ± 10.1 years) were evaluated. The 80-kVp protocol showed non-inferiority in lesion detectability and diagnostic ability for HCC (sensitivity, 85.7–89.3%; specificity, 96.3–98.6%) compared with the 120-kVp protocol (sensitivity, 91.0–93.3%; specificity, 93.6–97.3%) (p < 0.001–0.038). The ratio of fair image quality in the 80-kVp protocol also showed non-inferiority compared with that in the 120-kVp protocol in assessments by both readers (p < 0.001). HCC-to-liver contrast showed no significant differences for all phases (p = 0.309–0.705) between the two protocols. Conclusion: The 80-kVp protocol with hybrid iterative reconstruction for hepatic dynamic CT can decrease iodine doses while maintaining diagnostic performance and image quality compared with the 120-kVp protocol. Advances in knowledge: The 80- and 120-kVp protocols showed equivalent hepatic lesion detectability, diagnostic ability for HCC, image quality, and HCC-to-liver contrast. The 80-kVp protocol showed a 38.3% reduction in iodine dose compared with the 120-kVp protocol.

scholarly journals Pre and Postprocessing for JPEG to Handle Large Monochrome Images

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 255 ◽  
Author(s):  
Walaa Khalaf ◽  
Abeer Al Gburi ◽  
Dhafer Zaghar
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Reconstructed Image ◽  
Hyperbolic Function ◽  
Image Size ◽  
New Techniques ◽  
Large Size

Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.

scholarly journals Multi-Pooled Inception Features for No-Reference Image Quality Assessment

2020 ◽  
Vol 10 (6) ◽  
pp. 2186
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Input Image ◽  
Reference Image ◽  
Parameter Study ◽  
Image Size ◽  
In The Wild ◽  
Display Technology ◽  
Multi Level

Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score

2020 ◽  
Vol 42 (6) ◽  
pp. 271-283
Author(s):  
Puja Bharti ◽  
Deepti Mittal
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
Similarity Score ◽  
Input Image ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Low Contrast ◽  
Interactive Decision Making ◽  
Enhancement Method

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

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