Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sergey Kastryulin"'
Publikováno v:
IEEE Access, Vol 12, Pp 160946-160956 (2024)
This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fine-tuning. We eliminate the need for expressive textual embeddings by
Externí odkaz:
https://doaj.org/article/8cbe78d0a2184af7b27975094fd7503c
Publikováno v:
IEEE Access, Vol 11, Pp 14154-14168 (2023)
Image quality assessment (IQA) algorithms aim to reproduce the human’s perception of the image quality. The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performa
Externí odkaz:
https://doaj.org/article/8fc5b1b4524f4bb6b7568c796739ccd9
Autor:
Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus Van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. Van Osch, Elwin De Weerdt, Marius Staring
Publikováno v:
IEEE Access, Vol 8, Pp 204825-204838 (2020)
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data,
Externí odkaz:
https://doaj.org/article/723dd39f480f476b9cf7a6eac71a4db7
Publikováno v:
arXiv, 2022:2203.07809. Cornell University Library
IEEE Access, 11, 14154-14168. Institute of Electrical and Electronics Engineers
IEEE Access, 11, 14154-14168. Institute of Electrical and Electronics Engineers
Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performanc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf7a8073761af619f719a77a5a4dff33
https://research.tue.nl/nl/publications/4d0bed6d-4ffd-4237-a50b-6b43c686241d
https://research.tue.nl/nl/publications/4d0bed6d-4ffd-4237-a50b-6b43c686241d
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872304
MICCAI (6)
MICCAI (6)
We went below the MRI acceleration factors (a.k.a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge [29], and then used deep learning based image enhancement methods to compensate for the underres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faef4fbaf0ded245b9de71cfbcfc684e
https://doi.org/10.1007/978-3-030-87231-1_25
https://doi.org/10.1007/978-3-030-87231-1_25
Autor:
Jeroen Hendrikus Fransiscus Van Gemert, Elwin de Weerdt, Tim Nielsen, Mohamed S. Elmahdy, Boudewijn P. F. Lelieveldt, Sahar Yousefi, Nicola Pezzotti, Mariya Doneva, Christophe Schuelke, Marius Staring, Matthias J.P. van Osch, Sergey Kastryulin
Publikováno v:
IEEE Access, 8, 204825-204838. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE Access, Vol 8, Pp 204825-204838 (2020)
IEEE Access, 8
IEEE Access, Vol 8, Pp 204825-204838 (2020)
IEEE Access, 8
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ffbddabfb16b8e854bce8a89aa63fd
http://hdl.handle.net/1887/3184247
http://hdl.handle.net/1887/3184247