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Filters
Blind Filter Identification And Image Superresolution Using Subspace Methods
2007
Zenodo
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In this late approach, a crucial step is the use of a source separation technique. We investigate the possibility of using the subspace method in the context of image superresolution. ...
Limits of the Subspace Method In this section, we show that, for subsampled images, the subspace method is not sufficient to determine the filters, but provide an identification up to a (P 2 , P 2 ) mixing ...
doi:10.5281/zenodo.40423
fatcat:siq3mc4fqvcdvg7sn7ty7zpdgm
Superresolution imaging: a survey of current techniques
2008
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
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In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). ...
Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods. ...
BLIND SUPERRESOLUTION METHOD Blind superresolution (BSR) can find its roots in the related field of multiframe blind deconvolution (MBD). ...
doi:10.1117/12.797302
fatcat:yd2aqfldkfbd7br4vytt6vcrum
Subject index
1999
IEEE Transactions on Image Processing
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Active vision
Conjugate gradient methods
blind image deconvolution, recursive inverse filtering method. ...
., + , T-IP May 99 640-651 blind image deconvolution, recursive inverse filtering method. Chin Ann Ong, + , T-IP Jul 99 988-992 halftoning using wavelets. ...
doi:10.1109/tip.1999.806633
fatcat:wl55kzilqzd33dlz6kmq3cljdy
Data-driven multichannel superresolution with application to video sequences
1999
Optical Society of America. Journal A: Optics, Image Science, and Vision
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The estimated PSF's are then used to construct biorthogonal projection filters for the superresolution algorithm. This approach gives rise to a closed-form solution leading to a highspeed algorithm. ...
Unlike existing methods, where empirical models such as Gaussian, sinc, etc., are commonly used for characterizing channel PSF's, the PSF's are assumed unknown and possibly different and hence are blindly ...
INTRODUCTION Superresolution refers to methods for increasing the resolving power by recapturing additional high-frequency information with the use of image processing methods. ...
doi:10.1364/josaa.16.000481
fatcat:2nbdfhgg7radhhgu7x5qr7kjii
Enhanced Biggs–Andrews Asymmetric Iterative Blind Deconvolution
2006
Multidimensional systems and signal processing
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Simulations conducted on real-world and synthetic images confirm the importance of accurate support estimation in the blind superresolution problem. ...
Specifically, the Biggs-Andrews (B-A) multichannel iterative blind deconvolution (IBD) algorithm is modified to include the blur support estimation module and the asymmetry factor for the Richardson-Lucy ...
Acknowledgement The authors thank the two reviewers for their useful comments and their suggestions have been incorporated in this revised version. ...
doi:10.1007/s11045-005-6232-7
fatcat:fjy4adjpnrebrhrremmoa2li3m
Face recognition with independent component-based super-resolution
2006
Visual Communications and Image Processing 2006
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Therefore, we propose new superresolution algorithms using Bayesian estimation and projection onto convex sets methods in feature domain and present a comparative analysis of the proposed algorithms with ...
Hence, applying superresolution in this feature domain, in other words in face subspace, rather than in pixel domain, brings many advantages in computation together with robustness against noise and motion ...
Hasan Ates's useful discussions on set-theoretic approach. ...
doi:10.1117/12.645868
fatcat:b5qoubwdxbfyjb4e6zimmj5c2m
Structured Compressed Sensing: From Theory to Applications
2011
IEEE Transactions on Signal Processing
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Other Versions
Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. ...
In our overview, the theme is exploiting signal and measurement structure in compressive sensing. ...
ACKNOWLEDGMENT The authors would like to thank their colleagues for many useful comments and for their collaboration on many topics related to this review. In particular, they are grateful to M. ...
doi:10.1109/tsp.2011.2161982
fatcat:mlbtksjmqvbmfmi6gpxwtunbti
Inverse Synthetic Aperture Radar
2006
EURASIP Journal on Advances in Signal Processing
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Target classification and identification Radar signatures are often used for target classification and/or identification. ...
Such a limitation leads to the use of blind radial motion compensation (image autofocusing) and image formation processing that must deal with highly nonstationary signals. ...
His research interests are elastic and inelastic electromagnetic wave/matter interactions, and applications to electromagnetic imaging, measurement and superresolution techniques. ...
doi:10.1155/asp/2006/63465
fatcat:hchb6zadajer7a5ezwyq4mgxem
Hyperspectral Remote Sensing Data Analysis and Future Challenges
2013
IEEE Geoscience and Remote Sensing Magazine
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Very often, these applications rely on sophisticated and complex data analysis methods. ...
These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. ...
Visualization of hyperspectral AVIRIS image, from (a) to (d) using PCA and the methods of [20] - [22] , respectively. ...
doi:10.1109/mgrs.2013.2244672
fatcat:4tk7q6izd5hevhnrck36i5wkiy
Personal photo enhancement using example images
2010
ACM Transactions on Graphics
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We focus on correcting these types of images and use common faces across images to automatically perform both global and face-specific corrections. ...
We illustrate the power and generality of our approach by presenting a novel deblurring algorithm, and we show corrections that perform sharpening, superresolution, in-painting of over-and underexposured ...
In Figures 1 and 10 , we show two examples of our automatic blind deblurring method using our personal prior. In Figure 11 we compare our method to using Fergus et al.'s [2006] method. ...
doi:10.1145/1731047.1731050
fatcat:zsgnry5n4bgjdmqvnfyjbauxzy
2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., and Lopez, J.F ...
., +, JSTARS June 2019
1866-1881
Filtering
Contextual Filtering Methods based on the Subbands and Subspaces Decom-
position of Complex SAR Interferograms. ...
., +, JSTARS
Dec. 2019 5321-5333
Filtering algorithms
Contextual Filtering Methods based on the Subbands and Subspaces Decom-
position of Complex SAR Interferograms. ...
doi:10.1109/jstars.2020.2973794
fatcat:sncrozq3fjg4bgjf4lnkslbz3u
Face Recognition in Low Quality Images: A Survey
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1805.11519v1 fatcat:ol6326fqfjhsbjb5sfgdzv2f6eOther Versions
In addition to describing the methods, we also focus on datasets and experiment settings. ...
We further address the related works on unconstrained low-resolution face recognition and compare them with the result that use synthetic low-resolution data. ...
This could be used for restoring the image for identification. ...
arXiv:1805.11519v3
fatcat:izpl554u3fga5d62e6jxw4zuwu
2010 Index IEEE Transactions on Signal Processing Vol. 58
2010
IEEE Transactions on Signal Processing
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., +, TSP Dec. 2010 6181-6194 Learning Sparse Representation Using Iterative Subspace Identification. Gowreesunker, B. ...
-L., +, TSP Oct. 2010 5151-5164
Learning Sparse Representation Using Iterative Subspace Identification.
Gowreesunker, B. ...
Global Positioning System A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation. ...
doi:10.1109/tsp.2010.2092533
fatcat:4y66ezuo7zf6doe6nwjqwtc42i
Table of Contents
2021
IEEE Transactions on Signal Processing
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Elvira and I. Santamaria Blind Localization of Early Room Reflections Using Phase Aligned Spatial Correlation . . . . . . T. Shlomo and B. ...
Beferull-Lozano, and C. Asensio-Marco Adaptive Superresolution in Deconvolution of Sparse Peaks . . . . . . . . . . . . . . . . . . . . . A. Koulouri, P. Heins, and M. ...
doi:10.1109/tsp.2021.3136798
fatcat:kzkdhzcz3fgx3jv6gfjofooseq
Table of Contents
2021
IEEE Transactions on Signal Processing
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Cai, and D. Bi Optimization Methods for Signal Processing Adaptive Superresolution in Deconvolution of Sparse Peaks . ...
Rusu and L. Rosasco Blind Reconstruction of BCH and RS Codes Using Single-Error Correction . . . . . . . . . . . M. Song, J. Kim, and D.-J. ...
doi:10.1109/tsp.2021.3136800
fatcat:zhf46mb3rbdlnnh3u2xizgxof4
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