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Relevant bibliographies by topics / (110.3000) Image quality assessment

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Academic literature on the topic '(110.3000) Image quality assessment'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "(110.3000) Image quality assessment"

1

Haroutunian, Mariam E., David G. Asatryan, and Karen A. Mastoyan. "Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure." Mathematical Problems of Computer Science 61 (June 1, 2024): 7–14. http://dx.doi.org/10.51408/1963-0111.

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This research explores how different types of distorting algorithms impact the Full-Reference image quality assessment, particularly when subjective quality evaluations are incorporated. We draw upon the TID2013 database, which contains 3000 images distorted by 24 distinct algorithms, in conjunction with Mean Opinion Scores (MOS) for quality ratings. We compare the results of Normalized Mutual Information (NMI) for image quality score with W2, based on Weibull distribution, the common PSNR similarity measure and MOS. We advocate for integrating of NMI into the repertoire of image quality asses

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Romanyshyn, Yu, S. Yelmanov, T. Andrukhiv, and O. Teglivets. "IMAGE QUALITY ASSESSMENT BY CONVOLUTIONAL NEURAL NETWORK USING THE TID2013 DATABASE." Information and communication technologies, electronic engineering 3, no. 2 (2023): 170–79. http://dx.doi.org/10.23939/ictee2023.02.170.

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The article is devoted to the problem of automatic image quality assessment by a convolutional neural network when using the common TID2013 image database for training the neural network. The TID2013 database was chosen for the reason that it contains 25 base real-world images, which were distorted from these images using 24 different distortion methods and with 5 distortion levels, creating a sufficiently large database of 3000 images for training the neural network. For each image, an average expert assessment of its quality is given. All input images for the neural network are divided into

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3

Edom, A. O., G. B. Inah, and N. O. Egbe. "An Assessment of Image Quality of Radiographs in a Nigerian Teaching Hospital by Film Densitometry." Journal of Radiography and Radiation Sciences 23, no. 1 (2022): 1–6. http://dx.doi.org/10.48153/jrrs/2009/vrfp3522.

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3000 radiographs covering chest, upper limb, lower limb,lumbar spine (AP) and skull (PA) from the University ofCalabar teaching hospital (UCTH) were retrospectivelystudied by densitometry to assess the degree of variationin radiographic contrast over a ten year period (1999 â€“2008). The results show that variation in meanradiographic contrast from year to year was statisticallyinsignificant (p < 0.05) for the respective projectionsstudied. This result implies a relatively basicstandardisation in image quality arising from consistencyin technique acquired over time. The results would servea

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Asatryan, D. G., M. E. Harutyunyan, Y. I. Golub, and V. V. Starovoitov. "Influence of the distortion type on the image quality assessment when reducing its sizes." «System analysis and applied information science», no. 3 (September 25, 2020): 22–27. http://dx.doi.org/10.21122/2309-4923-2020-3-22-27.

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In this paper, the influence of various types of distortion of an image on its quality while reducing its sizes, is investigated. To assess the image quality, it is proposed to use the method of comparison with the standard using a previously developed measure based on the proximity of the values of the parameters of the Weibull distribution, which describes the gradient field of the image. The well-known TID2013 image database was used as the material, which includes 3000 images distorted by 24 types of distorting algorithms with five levels. Each image of the base was reduced by 2, 4 and 8 t

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Ciraj-Bjelac, Olivera, Milan Loncar, Dusko Kosutic, Milojko Kovacevic, and Danijela Arandjic. "Patient doses and image quality in chest radiography: The influence of different beam qualities." Nuclear Technology and Radiation Protection 22, no. 2 (2007): 48–52. http://dx.doi.org/10.2298/ntrp0702048c.

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A simple method of assessing optimal X-ray beam quality in respect to patient exposure and image quality in chest screen-film radiography is presented here. Different beam qualities were generated by the use of various combinations of tube voltages (70 kV to 110 kV) and Al and Cu filter thick nesses. Patient doses were assessed by kerma-area product measurements. Simultaneously, image quality was evaluated by a twofold method: a clinical study applying European quality criteria for the radiographic technique of image on image of 126 patients and a multifunctional home-made dosimetric phantom w

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Ablefoni, Maxime, Jakob Leonhardi, Constantin Ehrengut, et al. "Magnetic Resonance Imaging of Peritoneal Carcinomatosis: Evaluation of High b-Value Computed Diffusion-Weighted Imaging." Current Oncology 29, no. 7 (2022): 4593–603. http://dx.doi.org/10.3390/curroncol29070364.

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Over the last few years, diffusion-weighted imaging (DWI) has become increasingly relevant in the diagnostic assessment of peritoneal carcinomatosis. The aim of this study was to investigate the benefits of high-b DWI (c-DWI) compared to standard DWI in patients with peritoneal carcinomatosis. A cohort of 40 patients with peritoneal carcinomatosis were included in this retrospective study. DWI was performed with b-values of 50, 400, and 800 or 1000 s/mm² on a 1.5-T magnetic resonance imaging (MRI) scanner. C-DWI was calculated using a mono-exponential model with high b-values of 1000, 2000, 30

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7

Messai, Oussama, Aladine Chetouani, Fella Hachouf, and Zianou Ahmed Seghir. "Deep Quality evaluator guided by 3D Saliency for Stereoscopic Images." Electronic Imaging 2021, no. 11 (2021): 110–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.11.hvei-110.

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Due to the use of 3D contents in various applications, Stereo Image Quality Assessment (SIQA) has attracted more attention to ensure good viewing experience for the users. Several methods have been thus proposed in the literature with a clear improvement for deep learning-based methods. This paper introduces a new deep learning-based no-reference SIQA using cyclopean view hypothesis and human visual attention. First, the cyclopean image is built considering the presence of binocular rivalry that covers the asymmetric distortion case. Second, the saliency map is computed taking into account the

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Wang, Fengdan, Yan Zhang, Huadan Xue, et al. "Combined use of iterative reconstruction and monochromatic imaging in spinal fusion CT images." Acta Radiologica 58, no. 1 (2016): 62–69. http://dx.doi.org/10.1177/0284185116631182.

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Background Spinal fusion surgery is an important procedure for treating spinal diseases and computed tomography (CT) is a critical tool for postoperative evaluation. However, CT image quality is considerably impaired by metal artifacts and image noise. Purpose To explore whether metal artifacts and image noise can be reduced by combining two technologies, adaptive statistical iterative reconstruction (ASIR) and monochromatic imaging generated by gemstone spectral imaging (GSI) dual-energy CT. Material and Methods A total of 51 patients with 318 spinal pedicle screws were prospectively scanned

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da Costa, Douglas R., and Felipe A. Medeiros. "Big data for imaging assessment in glaucoma." Taiwan Journal of Ophthalmology 14, no. 3 (2024): 299–318. http://dx.doi.org/10.4103/tjo.tjo-d-24-00079.

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Abstract: Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vas

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10

Richard, S., and E. Samei. "WE-C-110-08: A Novel Phantom for CT Performance Assessment: Towards a Task-Based Measure of Image Quality." Medical Physics 38, no. 6Part32 (2011): 3810. http://dx.doi.org/10.1118/1.3613347.

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