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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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Single image super resolution for license plate
2010 Sixth International Conference on Natural Computation, 2010A single image super resolution algorithm for license plate preprocessing is proposed in this paper. The image to be enhanced is modeled as a Markov Random Field and is estimated from the input low resolution image by image patch pairs. From the input image and the training set, observation function and compatibility function can be calculated.
Yanbing Xue +3 more
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Non-parametric single image super resolution
The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, 2013In this paper, we introduce a single image super resolution based on non-parametric local information. The basic idea of the proposed method is to use a property, which is inferred by relations between input and its lower resolution images, of an unknown high resolution image.
Yunsang Han +2 more
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Single Image Super-Resolution With Multiscale Similarity Learning
IEEE Transactions on Neural Networks and Learning Systems, 2013Example learning-based image super-resolution (SR) is recognized as an effective way to produce a high-resolution (HR) image with the help of an external training set. The effectiveness of learning-based SR methods, however, depends highly upon the consistency between the supporting training set and low-resolution (LR) images to be handled.
Kaibing Zhang, Xinbo Gao, Dacheng Tao
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Edge-preserving single image super-resolution
Proceedings of the 19th ACM international conference on Multimedia, 2011This paper proposes a novel approach to single image super-resolution. First, an image up-sampling scheme is proposed which takes the advantages of both bilateral filtering and mean shift image segmentation. Then we use a shock filter to enhance strong edges in the initial up-sampling result and obtain an intermediate high-resolution image. Finally, we
Qiang Zhou +3 more
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Gradient boosting for single image super-resolution
Information Sciences, 2018Abstract The learning-based single image super-resolution (SISR) algorithm aims at recovering a high-resolution (HR) image from low-resolution (LR) input. The quality of the HR output mainly depends on the strength of the learning algorithms. Observing that gradient boosting is powerful in dealing with learning problems, we propose a new SISR ...
Dongping Xiong +3 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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A Unified Learning Framework for Single Image Super-Resolution
IEEE Transactions on Neural Networks and Learning Systems, 2014It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. However, learning-based methods are prone to introduce unexpected details into resultant HR images.
Xinbo Gao, Dacheng Tao, Xuelong Li
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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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