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Super-Resolution pedestrian re-identification method based on bidirectional generative adversarial network. [PDF]
PLoS OneWang Y, Wu Y.
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Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings. [PDF]
Eur Radiol ExpGreffier J +4 more
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Analysis of Single Image Super Resolution Models
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022Image Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image ...
Köprülü, Mertali +1 more
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Edge-Guided Single Depth Image Super Resolution
IEEE Transactions on Image Processing, 2014Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed.
, Jun Xie +2 more
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Single-molecule super-resolution imaging in bacteria
Current Opinion in Microbiology, 2012Bacteria have evolved complex, multi-component cellular machineries to carry out fundamental cellular processes such as cell division/separation, locomotion, protein secretion, DNA transcription/replication, or conjugation/competence. Diffraction of light has so far restricted the use of conventional fluorescence microscopy to reveal the composition ...
D I, Cattoni, J B, Fiche, M, Nöllmann
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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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Structure preserving single image super-resolution
2016 IEEE International Conference on Image Processing (ICIP), 2016In this paper, we present a novel structure preserving method for single image super-resolution to well construct edge structures and small detail structures. In our approach, the sharp edges are recovered via a novel edge preserving interpolation technique based on a well estimated gradient field and the edge preserving method, which incorporate the ...
Fan Yang +5 more
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Single Image Super-Resolution for SAR Images
2021Single image Super-Resolution (SR) is a method to get a high-resolution image out of a single Low-Resolution (LR) image. SR is used in different domains, such as medical imaging, satellite imaging, and security imaging. Using SR compared to LR images speeds up training convergence and boosts recognition and segmentation accuracy.
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Single-frame super resolution image embedding
2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015The multi-frame super resolutions have an admirable mathematical formulations and justification to combine the information of manifold LR images to get a HR one. Furthermore it has direct application for spatial video Super Resolution. Theoretically and sometimes for real images under global transformation those algorithm produces excellent results ...
Indu Sahu, Ashish Dewangan
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