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Epitomic Image Super-Resolution
Proceedings of the AAAI Conference on Artificial Intelligence, 2016We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch
Yingzhen Yang +6 more
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Contour enhanced image super-resolution
Journal of Visual Communication and Image Representation, 2022Linhua Kong +3 more
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Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, 2019
Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a
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Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a
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Image and Vision Computing, 2006
Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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Super-resolution Fluorescence Imaging
Our current understanding of living systems has advanced to the level of individual cells. However, there is still a pressing need to more precisely visualize the microstructure of cells, as well as the dynamic actions of biomolecules therein, including molecular translocation and chemical modifications (e.g.Hai-Hao Han, Xiao-Peng He
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Automated molecular-image cytometry and analysis in modern oncology
Nature Reviews Materials, 2020Ralph Weissleder, Hakho Lee
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Nature Methods, 2020Fabian Isensee +2 more
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