Artigos de revistas sobre o tema "No-Reference image quality assessment (NR-IQA)"
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Zhang, Haopeng, Bo Yuan, Bo Dong e Zhiguo Jiang. "No-Reference Blurred Image Quality Assessment by Structural Similarity Index". Applied Sciences 8, n.º 10 (22 de outubro de 2018): 2003. http://dx.doi.org/10.3390/app8102003.
Texto completo da fonteShi, Jinsong, Pan Gao e Jie Qin. "Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 5 (24 de março de 2024): 4829–37. http://dx.doi.org/10.1609/aaai.v38i5.28285.
Texto completo da fonteLee, Wonkyeong, Eunbyeol Cho, Wonjin Kim, Hyebin Choi, Kyongmin Sarah Beck, Hyun Jung Yoon, Jongduk Baek e Jang-Hwan Choi. "No-reference perceptual CT image quality assessment based on a self-supervised learning framework". Machine Learning: Science and Technology 3, n.º 4 (1 de dezembro de 2022): 045033. http://dx.doi.org/10.1088/2632-2153/aca87d.
Texto completo da fonteOszust, Mariusz. "No-Reference Image Quality Assessment with Local Gradient Orientations". Symmetry 11, n.º 1 (16 de janeiro de 2019): 95. http://dx.doi.org/10.3390/sym11010095.
Texto completo da fonteAhmed, Ismail Taha, Chen Soong Der, Baraa Tareq Hammad e Norziana Jamil. "Contrast-distorted image quality assessment based on curvelet domain features". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 3 (1 de junho de 2021): 2595. http://dx.doi.org/10.11591/ijece.v11i3.pp2595-2603.
Texto completo da fonteGarcia Freitas, Pedro, Luísa da Eira, Samuel Santos e Mylene Farias. "On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment". Journal of Imaging 4, n.º 10 (4 de outubro de 2018): 114. http://dx.doi.org/10.3390/jimaging4100114.
Texto completo da fonteGu, Jie, Gaofeng Meng, Cheng Da, Shiming Xiang e Chunhong Pan. "No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 8336–43. http://dx.doi.org/10.1609/aaai.v33i01.33018336.
Texto completo da fonteVarga, Domonkos. "No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion". Applied Sciences 12, n.º 1 (23 de dezembro de 2021): 101. http://dx.doi.org/10.3390/app12010101.
Texto completo da fonteYin, Guanghao, Wei Wang, Zehuan Yuan, Chuchu Han, Wei Ji, Shouqian Sun e Changhu Wang. "Content-Variant Reference Image Quality Assessment via Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junho de 2022): 3134–42. http://dx.doi.org/10.1609/aaai.v36i3.20221.
Texto completo da fonteGavrovska, Ana, Dragi Dujković, Andreja Samčović, Yuliya Golub e Valery Starovoitov. "Quadratic fitting model in no-reference image quality assessment". Telfor Journal 15, n.º 2 (2023): 32–37. http://dx.doi.org/10.5937/telfor2302032g.
Texto completo da fonteVarga, Domonkos. "No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features". Journal of Imaging 7, n.º 7 (10 de julho de 2021): 112. http://dx.doi.org/10.3390/jimaging7070112.
Texto completo da fonteYan, Chenggang, Tong Teng, Yutao Liu, Yongbing Zhang, Haoqian Wang e Xiangyang Ji. "Precise No-Reference Image Quality Evaluation Based on Distortion Identification". ACM Transactions on Multimedia Computing, Communications, and Applications 17, n.º 3s (31 de outubro de 2021): 1–21. http://dx.doi.org/10.1145/3468872.
Texto completo da fonteStępień, Igor, e Mariusz Oszust. "A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images". Journal of Imaging 8, n.º 6 (4 de junho de 2022): 160. http://dx.doi.org/10.3390/jimaging8060160.
Texto completo da fonteYe, Zhongchang, Xin Ye e Zhonghua Zhao. "Hybrid No-Reference Quality Assessment for Surveillance Images". Information 13, n.º 12 (16 de dezembro de 2022): 588. http://dx.doi.org/10.3390/info13120588.
Texto completo da fonteFu, Hao, Guojun Liu, Xiaoqin Yang, Lili Wei e Lixia Yang. "Two Low-Level Feature Distributions Based No Reference Image Quality Assessment". Applied Sciences 12, n.º 10 (14 de maio de 2022): 4975. http://dx.doi.org/10.3390/app12104975.
Texto completo da fonteGuan, Xiaodi, Fan Li e Lijun He. "Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels". Electronics 9, n.º 2 (3 de fevereiro de 2020): 252. http://dx.doi.org/10.3390/electronics9020252.
Texto completo da fonteVarga, Domonkos. "No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features". Journal of Imaging 6, n.º 8 (30 de julho de 2020): 75. http://dx.doi.org/10.3390/jimaging6080075.
Texto completo da fonteAhmed, Ismail Taha, Chen Soong Der, Norziana Jamil e Mohamad Afendee Mohamed. "Improve of contrast-distorted image quality assessment based on convolutional neural networks". International Journal of Electrical and Computer Engineering (IJECE) 9, n.º 6 (1 de dezembro de 2019): 5604. http://dx.doi.org/10.11591/ijece.v9i6.pp5604-5614.
Texto completo da fonteStępień, Igor, e Mariusz Oszust. "No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations". Remote Sensing 14, n.º 5 (24 de fevereiro de 2022): 1119. http://dx.doi.org/10.3390/rs14051119.
Texto completo da fonteRyu, Jihyoung. "Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism". Applied Sciences 13, n.º 4 (19 de fevereiro de 2023): 2682. http://dx.doi.org/10.3390/app13042682.
Texto completo da fonteVarga, Domonkos. "A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors". Sensors 22, n.º 18 (7 de setembro de 2022): 6775. http://dx.doi.org/10.3390/s22186775.
Texto completo da fonteRyu, Jihyoung. "A Visual Saliency-Based Neural Network Architecture for No-Reference Image Quality Assessment". Applied Sciences 12, n.º 19 (23 de setembro de 2022): 9567. http://dx.doi.org/10.3390/app12199567.
Texto completo da fonteAbdalmajeed, Saifeldeen, e Jiao Shuhong. "Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently". Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/389504.
Texto completo da fonteMahmood, Saifeldeen Abdalmajeed. "Three Different Features Based Metric To Assess Image Quality Blindly". FES Journal of Engineering Sciences 8, n.º 2 (23 de maio de 2020): 97–103. http://dx.doi.org/10.52981/fjes.v8i2.121.
Texto completo da fonteLU, WEN, LIHUO HE, WENJIAN TANG, FEI GAO e WEILONG HOU. "A NOVEL COMPRESSED IMAGES QUALITY METRIC". International Journal of Image and Graphics 11, n.º 02 (abril de 2011): 281–92. http://dx.doi.org/10.1142/s021946781100410x.
Texto completo da fonteUllah, Hayat, Muhammad Irfan, Kyungjin Han e Jong Weon Lee. "DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment". Sensors 20, n.º 22 (12 de novembro de 2020): 6457. http://dx.doi.org/10.3390/s20226457.
Texto completo da fonteZhang, Run, e Yongbin Wang. "Natural Image Quality Assessment Based on Visual Biological Cognitive Mechanism". International Journal of Software Innovation 7, n.º 1 (janeiro de 2019): 1–26. http://dx.doi.org/10.4018/ijsi.2019010101.
Texto completo da fonteWang, Yue, Zeng Gang Lin e Zi Cheng Liao. "Image Quality Assessment Based on Region of Interest". Applied Mechanics and Materials 596 (julho de 2014): 350–54. http://dx.doi.org/10.4028/www.scientific.net/amm.596.350.
Texto completo da fonteHan, Lintao, Hengyi Lv, Yuchen Zhao, Hailong Liu, Guoling Bi, Zhiyong Yin e Yuqiang Fang. "Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment". Sensors 23, n.º 1 (30 de dezembro de 2022): 427. http://dx.doi.org/10.3390/s23010427.
Texto completo da fonteStępień, Igor, Rafał Obuchowicz, Adam Piórkowski e Mariusz Oszust. "Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment". Sensors 21, n.º 4 (3 de fevereiro de 2021): 1043. http://dx.doi.org/10.3390/s21041043.
Texto completo da fonteCui, Yueli. "No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion". Entropy 22, n.º 3 (17 de março de 2020): 344. http://dx.doi.org/10.3390/e22030344.
Texto completo da fonteQian, Qi, e Qingbing Sang. "No-reference image quality assessment based on automatic machine learning". ITM Web of Conferences 45 (2022): 01034. http://dx.doi.org/10.1051/itmconf/20224501034.
Texto completo da fonteChandler, Damon M. "Seven Challenges in Image Quality Assessment: Past, Present, and Future Research". ISRN Signal Processing 2013 (6 de fevereiro de 2013): 1–53. http://dx.doi.org/10.1155/2013/905685.
Texto completo da fonteVarga, Domonkos. "No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features". Journal of Imaging 8, n.º 6 (19 de junho de 2022): 173. http://dx.doi.org/10.3390/jimaging8060173.
Texto completo da fonteGupta, Praful, Christos Bampis, Jack Glover, Nicholas Paulter e Alan Bovik. "Multivariate Statistical Approach to Image Quality Tasks". Journal of Imaging 4, n.º 10 (12 de outubro de 2018): 117. http://dx.doi.org/10.3390/jimaging4100117.
Texto completo da fonteGao, Guoqing, Lingxiao Li, Hao Chen, Ning Jiang, Shuqi Li, Qing Bian, Hua Bao e Changhui Rao. "No-Reference Quality Assessment of Extended Target Adaptive Optics Images Using Deep Neural Network". Sensors 24, n.º 1 (19 de dezembro de 2023): 1. http://dx.doi.org/10.3390/s24010001.
Texto completo da fonteGu, Ke, Guangtao Zhai, Xiaokang Yang e Wenjun Zhang. "No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation". Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/436031.
Texto completo da fonteLei, Shu, Huang Zijian, Yan Jiebin e Fei Fengchang. "Super Resolution Image Visual Quality Assessment Based on Feature Optimization". Computational Intelligence and Neuroscience 2022 (20 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/1263348.
Texto completo da fonteVarga, Domonkos. "Multi-Pooled Inception Features for No-Reference Image Quality Assessment". Applied Sciences 10, n.º 6 (23 de março de 2020): 2186. http://dx.doi.org/10.3390/app10062186.
Texto completo da fonteHu, Kai, Yanwen Zhang, Feiyu Lu, Zhiliang Deng e Yunping Liu. "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization". Journal of Marine Science and Engineering 8, n.º 10 (25 de setembro de 2020): 741. http://dx.doi.org/10.3390/jmse8100741.
Texto completo da fonteCourtney, Jane. "SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired". Journal of Imaging 7, n.º 9 (30 de agosto de 2021): 168. http://dx.doi.org/10.3390/jimaging7090168.
Texto completo da fonteLi, Yuyan, Yubo Dong, Haoyong Li, Danhua Liu, Fang Xue e Dahua Gao. "No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning". Remote Sensing 16, n.º 10 (8 de maio de 2024): 1657. http://dx.doi.org/10.3390/rs16101657.
Texto completo da fonteWang, Bin. "An Image Quality Assessment Approach Based on Saliency Map in Space Domain". Advanced Materials Research 1006-1007 (agosto de 2014): 768–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.768.
Texto completo da fonteStarovoitov, V. V., e F. V. Starovoitov. "COMPARATIVE ANALYSIS OF NO-REFERENCE QUALITY MEASURES FOR DIGITAL IMAGES". «System analysis and applied information science», n.º 1 (4 de maio de 2017): 24–32. http://dx.doi.org/10.21122/2309-4923-2017-1-24-32.
Texto completo da fonteModak, Sourav, Jonathan Heil e Anthony Stein. "Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network". Remote Sensing 16, n.º 5 (1 de março de 2024): 874. http://dx.doi.org/10.3390/rs16050874.
Texto completo da fonteZhang, Lin, Xilin Yang, Lijun Zhang, Xiao Liu, Shengjie Zhao e Yong Ma. "Towards Automatic Image Exposure Level Assessment". Mathematical Problems in Engineering 2020 (23 de novembro de 2020): 1–14. http://dx.doi.org/10.1155/2020/2789854.
Texto completo da fonteJin, Chongchong, Zongju Peng, Wenhui Zou, Fen Chen, Gangyi Jiang e Mei Yu. "No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis". Entropy 23, n.º 6 (18 de junho de 2021): 770. http://dx.doi.org/10.3390/e23060770.
Texto completo da fonteSybingco, Edwin, e Elmer P. Dadios. "Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency". Journal of Advanced Computational Intelligence and Intelligent Informatics 22, n.º 5 (20 de setembro de 2018): 725–30. http://dx.doi.org/10.20965/jaciii.2018.p0725.
Texto completo da fonteZheng, Yuanfeng, Yuchen Yan e Hao Jiang. "Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network". Sensors 24, n.º 10 (13 de maio de 2024): 3090. http://dx.doi.org/10.3390/s24103090.
Texto completo da fonteIrshad, Muhammad, Camilo Sanchez-Ferreira, Sana Alamgeer, Carlos H. Llanos e Mylène C. Q. Farias. "No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns". Electronic Imaging 2021, n.º 9 (18 de janeiro de 2021): 265–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-265.
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