Results 31 to 40 of about 2,065,913 (297)
Joint Blind Super-Resolution and Shadow Removing
IEICE Transactions on Information and Systems, 2007 Most learning-based super-resolution methods neglect the illumination problem. In this paper we propose a novel method to combine blind single-frame super-resolution and shadow removal into a single operation. Firstly, from the pattern recognition viewpoint, blur identification is considered as a classification problem.
Jianping Qiao, Ju Liu, Yen-Wei Chen
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Temporal Kernel Consistency for Blind Video Super-Resolution [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accurately estimate the unknown downscaling kernel from a given low-resolution (LR) image in order to leverage the kernel during restoration.
Lichuan Xiang +4 more
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Bridging the resolution gap: correlative super-resolution imaging [PDF]
, 2019 This month’s Under the Lens discusses progress towards bridging the resolution gap in correlative super-resolution light and electron microscopy, and highlights its application for visualizing bacterial ...
Ian M. Dobbie, Dobbie, Ian M
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IET Image Processing, 2021 The current super‐resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory.
Xin Yang +3 more
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Unravelling the structure of viral replication complexes at super-resolution [PDF]
, 2013 This work was supported by Biotechnology and Biomedical Sciences Research Council grant BB/H018719/1During infection, many RNA viruses produce characteristic inclusion bodies that contain both viral and host components.
Karl J. Oparka +12 more
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Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
IEEE Access, 2022 Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the adaptive SR networks and a more accurately estimated ...
Youngsoo Kim +3 more
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Improving Real-World Blind Super-Resolution
, 2023 This research is funded by Univer sity of Science, VNU-HCM project CNTT 2023-0The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior knowledge of the degradation process that caused the image to ...
Vo, Khoa D., Bui, Len T.
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Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment [PDF]
, 2023 With the emergence of image super-resolution (SR) algorithm, how to blindly evaluate the quality of super-resolution images has become an urgent task.
Fu, Jun
core
Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization
Remote Sensing, 2021 The fusion of low spatial resolution hyperspectral images and high spatial resolution multispectral images in the same scenario is important for the super-resolution of hyperspectral images.
Jian Long, Yuanxi Peng
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Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on
Divya Mishra, Ofer Hadar
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