Auswahl der wissenschaftlichen Literatur zum Thema „No-Reference image quality assessment (NR-IQA)“
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Zeitschriftenartikel zum Thema "No-Reference image quality assessment (NR-IQA)"
Zhang, Haopeng, Bo Yuan, Bo Dong und Zhiguo Jiang. „No-Reference Blurred Image Quality Assessment by Structural Similarity Index“. Applied Sciences 8, Nr. 10 (22.10.2018): 2003. http://dx.doi.org/10.3390/app8102003.
Der volle Inhalt der QuelleShi, Jinsong, Pan Gao und Jie Qin. „Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 5 (24.03.2024): 4829–37. http://dx.doi.org/10.1609/aaai.v38i5.28285.
Der volle Inhalt der QuelleLee, Wonkyeong, Eunbyeol Cho, Wonjin Kim, Hyebin Choi, Kyongmin Sarah Beck, Hyun Jung Yoon, Jongduk Baek und Jang-Hwan Choi. „No-reference perceptual CT image quality assessment based on a self-supervised learning framework“. Machine Learning: Science and Technology 3, Nr. 4 (01.12.2022): 045033. http://dx.doi.org/10.1088/2632-2153/aca87d.
Der volle Inhalt der QuelleOszust, Mariusz. „No-Reference Image Quality Assessment with Local Gradient Orientations“. Symmetry 11, Nr. 1 (16.01.2019): 95. http://dx.doi.org/10.3390/sym11010095.
Der volle Inhalt der QuelleAhmed, Ismail Taha, Chen Soong Der, Baraa Tareq Hammad und Norziana Jamil. „Contrast-distorted image quality assessment based on curvelet domain features“. International Journal of Electrical and Computer Engineering (IJECE) 11, Nr. 3 (01.06.2021): 2595. http://dx.doi.org/10.11591/ijece.v11i3.pp2595-2603.
Der volle Inhalt der QuelleGarcia Freitas, Pedro, Luísa da Eira, Samuel Santos und Mylene Farias. „On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment“. Journal of Imaging 4, Nr. 10 (04.10.2018): 114. http://dx.doi.org/10.3390/jimaging4100114.
Der volle Inhalt der QuelleGu, Jie, Gaofeng Meng, Cheng Da, Shiming Xiang und Chunhong Pan. „No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 8336–43. http://dx.doi.org/10.1609/aaai.v33i01.33018336.
Der volle Inhalt der QuelleVarga, Domonkos. „No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion“. Applied Sciences 12, Nr. 1 (23.12.2021): 101. http://dx.doi.org/10.3390/app12010101.
Der volle Inhalt der QuelleYin, Guanghao, Wei Wang, Zehuan Yuan, Chuchu Han, Wei Ji, Shouqian Sun und Changhu Wang. „Content-Variant Reference Image Quality Assessment via Knowledge Distillation“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 3 (28.06.2022): 3134–42. http://dx.doi.org/10.1609/aaai.v36i3.20221.
Der volle Inhalt der QuelleGavrovska, Ana, Dragi Dujković, Andreja Samčović, Yuliya Golub und Valery Starovoitov. „Quadratic fitting model in no-reference image quality assessment“. Telfor Journal 15, Nr. 2 (2023): 32–37. http://dx.doi.org/10.5937/telfor2302032g.
Der volle Inhalt der QuelleDissertationen zum Thema "No-Reference image quality assessment (NR-IQA)"
Hettiarachchi, Don Lahiru Nirmal Manikka. „An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique“. University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.
Der volle Inhalt der QuelleNguyen, Tan-Sy. „A smart system for processing and analyzing gastrointestinal abnormalities in wireless capsule endoscopy“. Electronic Thesis or Diss., Paris 13, 2023. http://www.theses.fr/2023PA131052.
Der volle Inhalt der QuelleIn this thesis, we address the challenges associated with the identification and diagnosis of pathological lesions in the gastrointestinal (GI) tract. Analyzing massive amounts of visual information obtained by Wireless Capsule Endsocopy (WCE) which is an excellent tool for visualizing and examining the GI tract (including the small intestine), poses a significant burden on clinicians, leading to an increased risk of misdiagnosis.In order to alleviate this issue, we develop an intelligent system capable of automatically detecting and identifying various GI disorders. However, the limited quality of acquired images due to distortions such as noise, blur, and uneven illumination poses a significant obstacle. Consequently, image pre-processing techniques play a crucial role in improving the quality of captured frames, thereby facilitating subsequent high-level tasks like abnormality detection and classification. In order to tackle the issues associated with limitations in image quality caused by the aforementioned distortions, novel learning-based algorithms have been proposed. More precisely, recent advancements in the realm of image restoration and enhancement techniques rely on learning-based approaches that necessitate pairs of distorted and reference images for training. However, a significant challenge arises in WCE which is an excellent tool for visualizing and diagnosing GI disorders, due to the absence of a dedicated dataset for evaluating image quality. To the best of our knowledge, there currently exists no specialized dataset designed explicitly for evaluating video quality in WCE. Therefore, in response to the need for an extensive video quality assessment dataset, we first introduce the "Quality-Oriented Database for Video Capsule Endoscopy" (QVCED).Subsequently, our findings show that assessing distortion severity significantly improves image enhancement effectiveness, especially in the case of uneven illumination. To this end, we propose a novel metric dedicated to the evaluation and quantification of uneven illumination in laparoscopic or WCE images, by extracting the image's background illuminance and considering the mapping effect of Histogram Equalization. Our metric outperforms some state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods, demonstrating its superiority and competitive performance compared to Full-Reference IQA (FR-IQA) methods.After conducting the assessment step, we proceed to develop an image quality enhancement method aimed at improving the overall quality of the images. This is achieved by leveraging the cross-attention algorithm, which establishes a comprehensive connection between the extracted distortion level and the degraded regions within the images. By employing this algorithm, we are able to precisely identify and target the specific areas in the images that have been affected by distortions. This allows an appropriate enhancement tailored to each degraded region, thereby effectively improving the image quality.Following the improvement of image quality, visual features are extracted and fed into a classifier to provide a diagnosis through classification. The challenge in the WCE domain is that a significant portion of the data remains unlabeled. To overcome this challenge, we have developed an efficient method based on self-supervised learning (SSL) approach to enhance the performance of classification. The proposed method, utilizing attention-based SSL, has successfully addressed the issue of limited labeled data commonly encountered in the existing literature
Buchteile zum Thema "No-Reference image quality assessment (NR-IQA)"
Ahmed, Basma, Mohamed Abdel-Nasser, Osama A. Omer, Amal Rashed und Domenec Puig. „No-Reference Digital Image Quality Assessment Based on Structure Similarity“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210156.
Der volle Inhalt der QuelleAhmed, Basma, Osama A. Omer, Amal Rashed, Domenec Puig und Mohamed Abdel-Nasser. „Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220345.
Der volle Inhalt der QuelleAbdelouahad, Abdelkaher Ait, Mohammed El Hassouni, Hocine Cherifi und Driss Aboutajdine. „A New Image Distortion Measure Based on Natural Scene Statistics Modeling“. In Geographic Information Systems, 616–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch037.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "No-Reference image quality assessment (NR-IQA)"
Ariffin, Syed Mohd Zahid Syed Zainal, und Nursuriati Jamil. „Illumination Classification based on No-Reference Image Quality Assessment (NR-IQA)“. In the 2019 Asia Pacific Information Technology Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3314527.3314529.
Der volle Inhalt der QuelleDa Silva, Renato, Luiz Brito, Marcelo Albertini, Marcelo Do Nascimento und André Backes. „Using CNNs for Quality Assessment of No-Reference and Full-Reference Compressed-Video Frames“. In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13484.
Der volle Inhalt der QuelleLin, Kwan-Yee, und Guanxiang Wang. „Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning“. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00083.
Der volle Inhalt der QuelleGil, Adriano, Aasim Khurshid, Juliana Postal und Thiago Figueira. „Visual assessment of equirectangular images for virtual reality applications In Unity“. In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8337.
Der volle Inhalt der QuelleNarsaiah, D., R. Surender Reddy, Aruna Kokkula, P. Anil Kumar und A. Karthik. „A Novel Full Reference-Image Quality Assessment (FR-IQA) for Adaptive Visual Perception Improvement“. In 2021 6th International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021. http://dx.doi.org/10.1109/icict50816.2021.9358610.
Der volle Inhalt der QuelleZaytoon, Mohamed, und Marwan Torki. „The Effect of Non-Reference Point Cloud Quality Assessment (NR-PCQA) Loss on 3D Scene Reconstruction from a Single Image“. In 2023 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2023. http://dx.doi.org/10.1109/iscc58397.2023.10218197.
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