Progressive edge-sensing dynamic scene deblurring

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors. In recent years, the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied. We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams. Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges. The coarse-to-fine architecture restores the image structure, first performing global adjustments, and then performing local refinement. In this way, not only is global correlation considered, but also residual information is used to significantly improve image restoration and enhance texture details. Experimental results show quantitative and qualitative improvements over existing methods.

An Incremental Algorithm for Concept Lattice Based on SSIM

As an effective tool for data analysis, Formal Concept Analysis (FCA) is widely used in software engineering and machine learning. The construction of concept lattice is a key step of the FCA. How to effectively update the concept lattice is still an open, interesting and important issue. The main aim of this paper is to provide a solution to this problem. So, we propose an incremental algorithm for concept lattice based on image structure similarity (SsimAddExtent). In addition, we perform time complexity analysis and experiments to show effectiveness of algorithm.

An iterative reconstruction method for sparse-projection data for low-dose CT

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

Human DNA AI Model to Predict COVID-19 Symptomatic or Asymptomatic Percentages

The current paper proposes to use convolutional neural networks (CNN) to analyze human genome single nucleotide variants (SNVs) from nuclear deoxyribonucleic acid (DNA) and mitochondrial deoxyribonucleic acid (mtDNA) presented as a 2D image structure to understand if the answer to COVID-19 severities can be found in the human genome. That methodology was implemented with 447 Mexican population samples. From the results, two main groups were formed divided into symptomatic and asymptomatic cases composed of 80.986% and 19.014% respectively and the model was validated through an online survey of individuals, giving a 91.89% of accuracy.

The Effectiveness of Buckroyd’s Group-Based Therapeutic Approach on Increasing Efficiency Life Style and Improving Body Self-Image

Background: Group teaching can create empathy in teaching sessions. Therefore the objective of the present study was to investigate the effectiveness of team teaching based on Buckroyd’s Method on lifestyle, self-efficacy and body image structure of the women with obesity in the city of Isfahan. Methods: This study was quasi-experimental with pretest, posttest and control group and follow-up. The statistical population of the study included all people with obesity referring to clinics in the city of Isfahan from whom 30 women with obesity were selected through purposive sampling from Sepahan Health clinic in 2014 and then they were put into two groups of fifteen (experimental and control). A therapeutic intervention based on Buckroyd Method was administered on the experimental group during 16 two-hour sessions twice a week. The measurement instruments in this study were BMI scale, demographic information form, life style self-efficacy questionnaire and body image questionnaire. Descriptive statistics (mean and standard deviation) and inferential statistics (repeated measures variance) were used to test the hypotheses using SPSS software (version 23) at 0.05 level of error. Results: The results showed that team teaching based on Buckroyd Method has been effective on the increase of life style self-efficacy (p < 0.001) and the improvement of body image (p < 0.001) as the mean score of life style self-efficacy and body image structure have increased after team teaching at the post-test stage and it could maintain this increase in time (follow-up stage). Conclusion: Buckroyd Method can be employed in the increase of life style self-efficacy and body image structure in the obese women.

The Effectiveness of Buckroyd’s Group-Based Therapeutic Approach on Increasing Efficiency Life Style and Improving Body Self-Image

Background: Group teaching can create empathy in teaching sessions. Therefore the objective of the present study was to investigate the effectiveness of team teaching based on Buckroyd’s Method on lifestyle, self-efficacy and body image structure of the women with obesity in the city of Isfahan. Methods: This study was quasi-experimental with pretest, posttest and control group and follow-up. The statistical population of the study included all people with obesity referring to clinics in the city of Isfahan from whom 30 women with obesity were selected through purposive sampling from Sepahan Health clinic in 2014 and then they were put into two groups of fifteen (experimental and control). A therapeutic intervention based on Buckroyd Method was administered on the experimental group during 16 two-hour sessions twice a week. The measurement instruments in this study were BMI scale, demographic information form, life style self-efficacy questionnaire and body image questionnaire. Descriptive statistics (mean and standard deviation) and inferential statistics (repeated measures variance) were used to test the hypotheses using SPSS software (version 23) at 0.05 level of error. Results: The results showed that team teaching based on Buckroyd Method has been effective on the increase of life style self-efficacy (p < 0.001) and the improvement of body image (p < 0.001) as the mean score of life style self-efficacy and body image structure have increased after team teaching at the post-test stage and it could maintain this increase in time (follow-up stage). Conclusion: Buckroyd Method can be employed in the increase of life style self-efficacy and body image structure in the obese women.

Co-occurrence network of TV advertisements revealing Japanese lifestyle

The relationship between culture and the appeals in TV advertisements has been extensively studied. We attempted to reveal the image structure produced by TV commercials in Japan, which may show the cultural features of the country, and to evaluate its temporal change. For this purpose, we constructed and analysed a co-occurrence network of keywords related to TV commercials by using immense data that include the records of all TV commercials aired in the Kanto area in Japan including Tokyo for a period of 15 years. We found a strong heterogeneity of the co-occurrence relationship, where a few keywords, e.g., ‘woman’, ‘man’, ‘animation’, and ‘logo’, co-occur with a huge number of other keywords every year. A community on a co-occurrence network can be regarded as a set of keywords that are mutually associated with each other through TV commercials. We examined the characteristics of the communities by associating them with categories of advertised products and found a temporal change in which the relationship between the communities possessing the image of entertainment and children and the category of PC and A/V gradually increases in strength. However, there was a consistent tendency in the examined period for the product categories related to communities that include ‘man’ to be less associated with those that include ‘woman’ and vice versa, which implicates a gender role inequality underlying the various appeals in TV commercials.

Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.

Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.