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Blind Filter Identification And Image Superresolution Using Subspace Methods

Muriel Gastaud, Saïd Ladjal, Henri Maitre
2007 Zenodo  
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

G. Cristóbal, E. Gil, F. Šroubek, J. Flusser, C. Miravet, F. B. Rodríguez, Franklin T. Luk
2008 Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII  
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  
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

H. Shekarforoush, R. Chellappa
1999 Optical Society of America. Journal A: Optics, Image Science, and Vision  
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

Mahesh B. Chappalli, N. K. Bose
2006 Multidimensional systems and signal processing  
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

Osman G. Sezer, Yucel Altunbasak, Aytul Ercil, John G. Apostolopoulos, Amir Said
2006 Visual Communications and Image Processing 2006  
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

Marco F. Duarte, Yonina C. Eldar
2011 IEEE Transactions on Signal Processing  
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

Marco Martorella, John Homer, James Palmer, Victor Chen, Fabrizio Berizzi, Brad Littleton, Dennis Longstaff
2006 EURASIP Journal on Advances in Signal Processing  
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

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
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

Neel Joshi, Wojciech Matusik, Edward H. Adelson, David J. Kriegman
2010 ACM Transactions on Graphics  
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  
., 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]

Pei Li, Loreto Prieto, Domingo Mery, Patrick Flynn
2019 arXiv   pre-print
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  
., +, 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  
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  
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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