1,720,953 research outputs found
Development of Efficient Image Upscaling Techniques
No full textImage upscaling is a popular topic in recent years, and it is used to create a high resolution (aHR) image from low resolution (LR) image data. An efficient image upscaling approach must preserve the original LR image’s edge information, texture, geometrical regularities, and smoothness while producing its HR counterpart. The most typical use of image upscaling is to improve the visual effect of a digital image after resizing it for displaying and printing. Image upscaling uses a variety of polynomial interpolation algorithms due to their low computational complexity and applicability for a wide range of real-time applications. A polynomial interpolation approach uses the weighted average or convolution of surrounding pixels to obtain the interpolated value at a specific place. This might cause blurring effects in upscaled images due to high frequency deterioration. This issue can be addressed by utilizing edge-directed algorithms that maintain high frequency information in an upscaled image for improved visual quality. Although edge-directed interpolation strategies are effective at preserving fine details and edge information in an image during upscaling, they are computationally more complex than polynomial interpolation schemes due to the usage of adaptive and local-based techniques. Most transform-domain interpolation algorithms in the literature produce blurring effects in upscaled images, particularly at edges and high-variance regions. Learning-based picture interpolation algorithms can produce high-quality results with fine features, but they often require significant computing resources and training data. This dissertation suggests pre-processing approaches to reduce blurring effects in upscaled images, incorporating an improved discrete cosine transform that recovers lost information due to upscaling using a bilateral filter. The weighted missing details are then integrated with the LR image before interpolation, resulting in less blurring in the high-variance region. However, in the following method, a higher order Laplacian filter is used to sharpen the edge presence in each direction of the degraded image in order to predict small details before combining them into an LR image. This strategy reduces blurring caused by interpolation. However, with iterative optimization sharpening, missing details are sharpened repeatedly using an optimal filter before interpolation, resulting in a better recovered HR image with more detailed information. However, an effective method of image upscaling is suggested here that combines iterative-back projection to reduce blurring effects with a convolutional neural network to retrieve both shallow and deep data separately. Some post-processing solutions have also been proposed, including an improved transform-domain approach that employs discrete sine transform upscaling to improve the quality of the HR image via difference image after upscaling. Another cubic B-spline spatial-domain approach involves sharpening degraded high frequency data and combining it with an upscaled image to get the restored HR image. Then comes a new technique: adaptive edge sharpening-optimized directional anisotropic diffusion, in which the smooth and edge areas are treated individually after upscaling to eliminate blurring effects and improve fine details. Several hybrid techniques have been developed to reduce the blurring effect in interpolated images. Hybrid approaches are developed by merging pre- and post-processing algorithms. In optimal local adaptive edge preserving spline, the high frequency details of an LR image are increased to compensate for blurring in the equivalent upscaled image. Furthermore, edge expansion is used to anticipate high frequency features with local statistics, preserving the edge contents in the upscaled image. However, the edge-error (EE) method uses the LR image’s edge to guide interpolation, whereas the edge-residual (ER) approach uses both the LR edge and lost information, followed by sharpening with a higher order filter. The restored HR image is generated by combining the sharpened image and the interpolated image
Performance Analysis of HE Methods for Low Contrast Images
No full textAbstractThe image enhancement is one of the important issues in image processing. The main purpose is to highlight certain characteristic of image such as: contrast, sharpening. Histogram equalization is the well-known method for image enhancement. Histogram equalization became a popular technique because it is simple and effective. However Histogram equalization cause excessive contrast enhancement which cause visual artifacts of processed image. In this paper new forms of histogram equalization are overviewed to overcome this drawback. The major difference among the methods is the way to divide the input histogram. Recursive exposure based sub-image histogram equalization (R_ESIHE) use average intensity value as the separating point. Median-mean based sub-image clipped histogram equalization (MMSICHE) and Quadrants dynamic histogram equalization for contrast enhancement (QDHE) use median intensity value as separating point. Here objective parameters are Peak signal to noise ratio (PSNR) and Absolute Mean Brightness Error (AMBE)used to compare the quality of enhancement
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
