mapSR: A Deep Neural Network for Super-Resolution of Raster Map
ISPRS International Journal of Geo-Information, 2023 The purpose of multisource map super-resolution is to reconstruct high-resolution maps based on low-resolution maps, which is valuable for content-based map tasks such as map recognition and classification.
Honghao Li, Xiran Zhou, Zhigang Yan
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NeuroImage, 2021 Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths.
Geraline Vis +3 more
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Super-resolution reconstruction of rock CT images based on Real-ESRGAN
Gong-kuang zidonghua, 2023 Due to factors such as image acquisition equipment and geological environment, rock CT images have low resolution and unclear details. However, existing image super-resolution reconstruction methods are prone to losing details when characterizing high ...
LI Gang +6 more
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network [PDF]
, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Aitken, AP +7 more
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Super-resolution Reconstruction of Remote Sensing Images Based on Joint Nonnegative Dictionary Learning [PDF]
Jisuanji gongcheng, 2016 Aiming at the super-resolution reconstruction of images,a super-resolution reconstruction algorithm for a single image based on joint nonnegative dictionary learning is proposed in this paper,and it is applied in the super-resolution reconstruction of ...
WEI Wei,WU Kongping,GUO Laigong,QIN Meng
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Single image super resolution via sparse reconstruction [PDF]
SPIE Proceedings, 2012 High resolution sensors are required for recognition purposes. Low resolution sensors, however, are still widely used. Software can be used to increase the resolution of such sensors. One way of increasing the resolution of the images produced is using multi-frame super resolution algorithms. Limitation of these methods are that the reconstruction only
Kruithof, M.C. +3 more
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FRESH – FRI-based single-image super-resolution algorithm [PDF]
, 2016 In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets.
Dragotti, P, Wei, X
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Single image super resolution via neighbor reconstruction [PDF]
Pattern Recognition Letters, 2019 Super Resolution (SR) is a complex, ill-posed problem where the aim is to construct the mapping between the low and high resolution manifolds of image patches. Anchored neighborhood regression for SR (namely A+ [27]) has shown promising results. In this paper we present a new regression-based SR algorithm that overcomes the limitations of A+ and ...
Zhihong Zhang +7 more
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Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction. [PDF]
AJNR Am J NeuroradiolZhang S +12 more
europepmc +2 more sources
Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing [PDF]
, 2018 There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth.
Fang SHANG +3 more
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