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https://www.researchgate.net/publication/6289713_Image_Superresolution_Using_Support_Vector_Regression
After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and ...
https://kuleshov.github.io/audio-super-res/
Using deep convolutional neural networks to upsample audio signals such as speech or music. Code Paper We train neural networks to impute new time-domain samples in an audio signal; this is similar to the image super-resolution problem, where individual audio samples are analogous to pixels.
https://arxiv.org/abs/1807.02758
Jul 08, 2018 · Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we …Cited by: 60
https://en.wikipedia.org/wiki/Super-resolution_imaging
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced.. In some radar and sonar imaging applications (e.g. magnetic resonance imaging (MRI), high-resolution computed tomography), subspace ...
https://ieeexplore.ieee.org/document/7115171/
Abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.Cited by: 2464
https://arxiv.org/abs/1609.04802
Sep 15, 2016 · Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective ...Cited by: 1361
https://www.microsoft.com/en-us/research/publication/bayesian-image-super-resolution/
The extraction of a single high-quality image from a set of low-resolution images is an important problem which arises in fields such as remote sensing, surveillance, medical imaging and the extraction of still images from video. Typical approaches are based on the use of cross-correlation to register the images followed by the inversion of the […]Cited by: 509
https://download.cnet.com/QE-SuperResolution/3000-12511_4-10392416.html
QE SuperResolution is a tool for enhancing the quality and resolution of images. It is based upon combining image information from several similar images taken from a movie sequence, or ...Subcategory: Digital Photo Tools
https://dl.acm.org/citation.cfm?id=2321380
After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets.Cited by: 270
http://www.columbia.edu/~jw2966/papers/YWHM10-TIP.pdf
1 Image Super-Resolution via Sparse Representation Jianchao Yang, Student Member, IEEE, John Wright, Student Member, IEEE Thomas Huang, Life Fellow, IEEE and Yi Ma, Senior Member, IEEE Abstract—This paper presents a new approach to single-image superresolution, based on sparse signal representation. Research
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