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Deep Blind Hyperspectral Image Super-Resolution
IEEE Transactions on Neural Networks and Learning Systems, 2021The production of a high spatial resolution (HR) hyperspectral image (HSI) through the fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has underpinned much of the recent progress in HSI super-resolution. The premise of these signs of progress is that both the degeneration from the HR HSI to LR HSI in the spatial domain ...
Lei Zhang 0054 +4 more
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Fast and Robust ADMM for Blind Super-Resolution
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Though the blind super-resolution problem is nonconvex in nature, recent advance shows the feasibility of a convex formulation which gives the unique recovery guarantee. However, the convexification procedure is coupled with a huge computational cost and is therefore of great interests to investigate fast algorithms.
Yifan Ran, Wei Dai 0001
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Non-stationary blind super-resolution
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016In this paper, we propose a new framework for parameter estimation of complex exponentials from their modulations with unknown waveforms via convex programming. Our model generalizes the recently developed blind sparse spike deconvolution framework by Y. Chi [1] to the non-stationary scenario and encompasses a wide spectrum of applications.
Dehui Yang +2 more
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Blind Super-Resolution Approach for Exploiting Illumination Variety in Optical-Lattice Illumination Microscopy [PDF]
ACS Photonics, 2021 Optical-lattice illumination patterns help in pushing high spatial frequency components of the sample into the optical transfer function of a collection microscope.
Krishna Agarwal +2 more
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Patch based blind image super resolution
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. We show
Qiang Wang 0023, Xiaoou Tang, Harry Shum
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Blind Super-resolution of Faces for Surveillance
2021Super-resolution (SR) refers to a class of techniques that derive a high resolution image from its low resolution (LR) counterpart. A vast amount of literature exists on SR spanning both multi and single image approaches.
T. M. Nimisha, A. N. Rajagopalan
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KernelNet: A Blind Super-Resolution Kernel Estimation Network
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021Recently developed deep neural network methods have achieved remarkable performance in the Super Resolution problem when applied to Low Resolution (LR) images that are obtained from High Resolution (HR) images with ideal and predefined downsampling processing, i.e., convolution with a known blurring kernel that is followed by subsampling (e.g., Bicubic)
Mehmet Yamac, Baran Ataman, Aakif Nawaz
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MAP Based Blind Super-Resolution
2012 International Conference on Industrial Control and Electronics Engineering, 2012Super-resolution is the process of obtaining a high resolution image from multiple low resolution images. In most of the super-resolution algorithms, the blur parameter of a LR-image model always have to be manually set as a default value, this is not a good solution.
Liu Gang, Hu Zhenlong
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Blind super-resolution of sparse spike signals
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015In many applications, the observations can be modeled as a linear combination of a small number of scaled and shifted copies of a bandlimited point spread function, either determined by the nature or designed by the users. Examples include neural spike trains, returns in radar and sonar, images in astronomy and single-molecule microscopy, etc. It is of
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Blind Super-resolution in Two-dimensional Parameter Space
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019In this paper, we provide a new mathematical framework for identifying the parameters of a linear system from its response to multiple unknown waveforms. We assume that the system response is given by an unknown number of scaled versions of time-delayed and frequency-shifted unknown waveforms.
Mohamed A. Suliman, Wei Dai 0001
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