Zobrazeno 1 - 10
of 615
pro vyhledávání: '"video super-resolution"'
Autor:
Mansoor Hayat, Supavadee Aramvith
Publikováno v:
IEEE Access, Vol 12, Pp 30893-30906 (2024)
Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a significant revolution in medical sciences. This advancement gives surgeons and physicians a deeper understanding of patients’ organ anatomy. However, like any tec
Externí odkaz:
https://doaj.org/article/29ab7d26d0bc4743a605f13bb94ebe00
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 685-695 (2024)
Intelligent processing and analysis of satellite video has become one of the research hotspots in the representation of remote sensing, and satellite video super-resolution (SVSR) is an important research direction, which can improve the image qualit
Externí odkaz:
https://doaj.org/article/2170524eed834effae907be9f5735f5b
Publikováno v:
Virtual Reality & Intelligent Hardware, Vol 5, Iss 6, Pp 523-537 (2023)
Background: The recurrent recovery is one of the common methods for video super-resolution, which models the correlation between frames via hidden states. However, when we apply the structure to real-world scenarios, it leads to unsatisfactory artifa
Externí odkaz:
https://doaj.org/article/5959cd1407c64f3da9470e79fc4df7cf
Publikováno v:
Autonomous Intelligent Systems, Vol 3, Iss 1, Pp 1-9 (2023)
Abstract Space-time video super-resolution (STVSR) serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts. Recent approaches utilize end-to-end deep learning models to achieve ST
Externí odkaz:
https://doaj.org/article/c1c79180f6df4b89be20519cf8c21680
Autor:
Mingxuan Lu, Peng Zhang
Publikováno v:
Sensors, Vol 24, Iss 7, p 2211 (2024)
Video super-resolution (VSR) remains challenging for real-world applications due to complex and unknown degradations. Existing methods lack the flexibility to handle video sequences with different degradation levels, thus failing to reflect real-worl
Externí odkaz:
https://doaj.org/article/2f75573ccef8490988e60f3bcb731ca0
Publikováno v:
IEEE Access, Vol 11, Pp 122586-122597 (2023)
Video super-resolution technology enhances the display quality of videos by obtaining high-resolution videos from low-resolution videos. Unlike single-image super-resolution, utilizing information between adjacent video frames is crucial in video sup
Externí odkaz:
https://doaj.org/article/b1d2176869c54c61acbdf95679bbb10c
Autor:
Guofang Li, Yonggui Zhu
Publikováno v:
IEEE Access, Vol 11, Pp 103476-103485 (2023)
Temporal modeling is the essential to achieve video super-resolution. Most models use alignment or recurrent methods to directly exploit the temporal information of consecutive frames. However, the feature information extracted directly from the inpu
Externí odkaz:
https://doaj.org/article/f77990f582934016bb76ae86226d91c5
Publikováno v:
IEEE Access, Vol 11, Pp 103419-103430 (2023)
Video Super-Resolution (VSR) is the task of reconstructing high-resolution (HR) video sequences from low-resolution (LR) video sequences. Apart from spatial information of reference frames, temporal information of neighboring frames is also important
Externí odkaz:
https://doaj.org/article/b7ccb6edbe97430b984897c7b6218718
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 4, Pp 3989-4002 (2022)
Abstract Video super-resolution (VSR) aims to recover the high-resolution (HR) contents from the low-resolution (LR) observations relying on compositing the spatial–temporal information in the LR frames. It is crucial to propagate and aggregate spa
Externí odkaz:
https://doaj.org/article/dec357697e284e2eb00265ad19f28438
Publikováno v:
Sensors, Vol 24, Iss 1, p 170 (2023)
Due to the proliferation of video data in Internet of Things (IoT) systems, in order to reduce the data burden, most social media platforms typically employ downsampling to reduce the resolution of high-resolution (HR) videos before video coding. Con
Externí odkaz:
https://doaj.org/article/02fffac7853d4e72a977e85e3aec22ac