scholarly journals Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

2021 ◽  
Author(s):  
Vlad Atanasiu ◽  
Isabelle Marthot-Santaniello
Keyword(s):  
Image Quality ◽  
User Experience ◽  
Visual Illusions ◽  
Light Spectrum ◽  
Color Processing ◽  
Social Conditioning ◽  
Visual Tasks ◽  
Enhancement Method ◽  
Expertise Level ◽  
Scholarly Editing

AbstractThis article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.

scholarly journals Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 570 ◽  
Author(s):  
Xuhui Ye ◽  
Gongping Wu ◽  
Le Huang ◽  
Fei Fan ◽  
Yongxiang Zhang
Keyword(s):  
Image Quality ◽  
Power Transmission ◽  
Low Frequency ◽  
Speckle Noise ◽  
Standard Test ◽  
Low Contrast ◽  
Inspection Robot ◽  
Fuzzy Enhancement ◽  
Enhancement Method ◽  
Frequency Components

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

scholarly journals Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yu Li ◽  
Hongfei Cao ◽  
Carla M. Allen ◽  
Xin Wang ◽  
Sanda Erdelez ◽  
...  
Keyword(s):  
Visual Attention ◽  
Computational Method ◽  
Human Cognition ◽  
Visual Knowledge ◽  
Spatial Features ◽  
Temporal Features ◽  
Visual Tasks ◽  
Visual Understanding ◽  
Reasoning Patterns ◽  
Expertise Level

AbstractVisual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

Research on Image Technology with Progressive Image Compression Based on JPEG and Laplacian Pyramid

2014 ◽  
Vol 886 ◽  
pp. 650-654
Author(s):  
Bo Hao Xu ◽  
Yong Sheng Hao
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
User Experience ◽  
Image Transmission ◽  
Laplacian Pyramid ◽  
Progressive Image Transmission ◽  
User Demand ◽  
Progressive Image Compression ◽  
Compression Coding

Progressive image transmission is a kind of image technology has been widely used in various fields, it can not only save bandwidth but also improve the user experience to meet user demand for different image quality. According to user's demand for image quality, realizing the progress of image compression coding flow can meet the demand of users. This article mainly introduce by means of JPEG and Laplacian pyramid coding principle implement progressive image compression.

Assessment of Some Enhancement Methods of Renal X-ray Image

2020 ◽  
Vol 18 (12) ◽  
pp. 01-05
Author(s):  
Salim J. Attia
Keyword(s):  
Image Quality ◽  
Radiation Dosage ◽  
Histogram Equalization ◽  
Quality Factors ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Quality Image ◽  
Enhancement Method ◽  
Spatial Quality

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

Evaluating image quality using consistent grey relational grade

2014 ◽  
Vol 31 (2) ◽  
pp. 231-249 ◽  
Author(s):  
Yen-Ching Chang ◽  
Chun-Ming Chang ◽  
Liang-Hwa Chen ◽  
Tung-Jung Chan
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Quality Evaluation ◽  
New Approach ◽  
Content Type ◽  
Multi Criteria Evaluation ◽  
Grey Relational Grade ◽  
Enhancement Method ◽  
Efficient Scheme ◽  
Grey Relational

Purpose – Assessing image quality is a difficult task. Different demands need distinct criteria, so it is not realistic to decide which contrast enhancement method is better only through one criterion. The main purpose is to propose an efficient scheme to effectively evaluate image quality. Furthermore, the idea can be applied in other fields. Design/methodology/approach – To objectively and quantitatively assess image quality, the authors integrate four criteria into one composite criterion and use it to evaluate seven existing contrast enhancement methods. The mechanism of integration is through a newly proposed way of computing a grey relational grade (GRGd), called the consistent grey relational grade (CGRGd). Findings – In this paper, the authors propose the CGRGd, which is more efficient and consistent than other existing GRGds. When applied to image quality evaluation, the proposed CGRGd can effectively choose the best method than others. The results also indicate that the proposed CGRGd combined with appropriate criteria can be widely used in the field of multiple criteria. Originality/value – The proposed CGRGd is a new approach to the problem of multi-criteria evaluation, and its application to the evaluation of image quality is a novel idea. For readers interested in the field of multi-criteria decision-making, the CGRGd provides an efficient and effective alternative.

scholarly journals Methods to Increase the Contrast of the Image with Preserving the Visual Quality

2021 ◽  
Vol 6 (2) ◽  
pp. 140-145
Author(s):  
Mykola Maksymiv ◽  
◽  
Taras Rak
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Image Contrast ◽  
Weighted Sum ◽  
Enhancement Method ◽  
Optimal Target ◽  
Statistical Metrics

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.

Results of a study of remote reporting over virtual applications and using visually calibrated monitors

2021 ◽  
pp. 201010582110661
Author(s):  
Yusheng Keefe Lai ◽  
Benjamin Jyhhan Kuo ◽  
Kheng Choon Lim ◽  
Chee Yeong Lim ◽  
Albert Su Chong Low ◽  
...  
Keyword(s):  
Image Quality ◽  
User Experience ◽  
Imaging Study ◽  
Image Manipulation ◽  
Final Report ◽  
Significant Discrepancy ◽  
Local Leadership ◽  
Factors Influencing ◽  
Average Image ◽  
Average Image Quality

Background and Objectives The purpose of this study is to examine differences in image quality, discrepancy rates, productivity and user experience between remote reporting over Virtual Application (VA) using visually calibrated monitors, and reporting using diagnostic grade workstations in hospital premises. Methods Three specialist accredited radiologists examined and provisionally reported outpatient CT and MR studies over PACS delivered as a VA, using visually calibrated monitors from their homes. They then proceeded to view the same studies within hospital premises and issue a final report. Surveys were filled out for each imaging study. Discrepancies were reviewed and assigned RADPEER scores. Results A total of 51 outpatient CT and MRIs were read. Relative to hospital premise reporting, on a Likert scale of 5 (the higher the better), average image quality was 3.9, speed of loading and image manipulation was 4.4 and productivity was 4.1. Remote reporting user experience did not differ significantly between CT versus MRI studies. Complete concordance rate was 80.4% (41/51) and only one of the studies had a significant discrepancy, which may have been due to extra time given to interpretation. All three radiologists reported factors influencing image display and quality as the top factor impacting remote reporting throughput. Conclusions Remote reporting over VA with visually calibrated monitors for CT and MR can be useful in periods of staffing difficulty to augment on-site radiologists, though attention must be paid to its limitations and policies defined by local leadership with reference to relevant national position

P-51: A Novel Image Quality Enhancement Method for AMOLEDs

2008 ◽  
Vol 39 (1) ◽  
pp. 1371
Author(s):  
Byung-Hwee Park ◽  
Seung-Kyu Kim ◽  
Chang-Hoon Jeon ◽  
Soon-Kwang Hong ◽  
Byeong-Koo Kim ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Enhancement ◽  
Image Quality Enhancement ◽  
Enhancement Method

scholarly journals LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1371
Author(s):  
Junshu Wang ◽  
Yue Yang ◽  
Yuan Chen ◽  
Yuxing Han
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Original Structure ◽  
Edge Image ◽  
Generative Adversarial Network ◽  
Test Set ◽  
Generalization Ability ◽  
Adversarial Network ◽  
Enhancement Method ◽  
Low Illumination

In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.

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