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J. Yen (1956)
On Nonuniform Sampling of Bandwidth-Limited SignalsIEEE Transactions on Circuits and Systems I-regular Papers, 3
Haoying Fu, M. Ng, M. Nikolova, J. Barlow (2005)
Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image RestorationSIAM J. Sci. Comput., 27
Andrew Patti, M. Sezan, A. Tekalp (1992)
High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blurProceedings of 1st International Conference on Image Processing, 1
Int. J. Imag. Syst. Technol
Sina Farsiu, D. Robinson, Michael Elad, P. Milanfar (2003)
Robust shift and add approach to superresolution, 5203
A. Rajagopalan, V. Kiran (2003)
Motion-free superresolution and the role of relative blur.Journal of the Optical Society of America. A, Optics, image science, and vision, 20 11
H. Ur, D. Gross (1992)
Improved resolution from subpixel shifted picturesCVGIP Graph. Model. Image Process., 54
S. Lertrattanapanich, N. Bose (2002)
High resolution image formation from low resolution frames using Delaunay triangulationIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 11 12
R. Chan, T. Chan, Lixin Shen, Zuowei Shen (2002)
Wavelet Algorithms for High-Resolution Image ReconstructionSIAM J. Sci. Comput., 24
M. Ng, A. Yip (2001)
A Fast MAP Algorithm for High-Resolution Image Reconstruction with MultisensorsMultidimensional Systems and Signal Processing, 12
T. Ranchin, B. Aiazzi, L. Alparone, S. Baronti, L. Wald (2003)
Image fusion—the ARSIS concept and some successful implementation schemesIsprs Journal of Photogrammetry and Remote Sensing, 58
Seunghyeon Rhee, M. Kang (1999)
Discrete cosine transform based regularized high-resolution image reconstruction algorithmOptical Engineering, 38
R. Schultz, R. Stevenson (1996)
Extraction of high-resolution frames from video sequencesIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 5 6
M. Irani, Shmuel Peleg (1991)
Improving resolution by image registrationCVGIP Graph. Model. Image Process., 53
R. Allen, S. Culley, B. Hicks (2001)
Informal information for web-based engineering catalogues, 4566
Luis Alvarez, J. Mateos, R. Molina, A. Katsaggelos (2004)
High‐resolution images from compressed low‐resolution video: Motion estimation and observable pixelsInternational Journal of Imaging Systems and Technology, 14
Sina Farsiu, Michael Elad, P. Milanfar (2006)
Multiframe demosaicing and super-resolution of color imagesIEEE Transactions on Image Processing, 15
Michael Elad, A. Feuer (1997)
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured imagesIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 6 12
C. Segall, R. Molina, A. Katsaggelos (2003)
High-resolution images from low-resolution compressed videoIEEE Signal Process. Mag., 20
M. González-Audícana, X. Otazu, O. Fors, J. Álvarez-Mozos (2006)
A low computational-cost method to fuse IKONOS images using the spectral response function of its sensorsIEEE Transactions on Geoscience and Remote Sensing, 44
David Capel, Andrew Zisserman (2003)
Computer vision applied to super resolutionIEEE Signal Process. Mag., 20
C. Segall, A. Katsaggelos, R. Molina, J. Mateos (2004)
Bayesian resolution enhancement of compressed videoIEEE Transactions on Image Processing, 13
(2005)
Super resolution and pansharpening of multispectral images
Sina Farsiu, D. Robinson, Michael Elad, P. Milanfar (2004)
Dynamic demosaicing and color superresolution of video sequences, 5562
IEEE Trans. Image Process
J. Park, M. Kang (2004)
Spatially adaptive multi-resolution multispectral image fusionInternational Journal of Remote Sensing, 25
A. Kumar, A. Kumar, R. Navalgund (2006)
Selection of IRS-P6 LISS-4 MO mode band for producing band-sharpened multispectral imageryIEEE Geoscience and Remote Sensing Letters, 3
M. Joshi, L. Bruzzone, S. Chaudhuri (2006)
A Model-Based Approach to Multiresolution Fusion in Remotely Sensed ImagesIEEE Transactions on Geoscience and Remote Sensing, 44
A. Zomet, Shmuel Peleg (2000)
Efficient super-resolution and applications to mosaicsProceedings 15th International Conference on Pattern Recognition. ICPR-2000, 1
A. Papoulis (1977)
Generalized sampling expansion
C. Latry, B. Rougé (2003)
Super resolution: quincunx sampling and fusion processingIGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 1
S. Park, Min Park, M. Kang (2003)
Super-resolution image reconstruction: a technical overviewIEEE Signal Process. Mag., 20
Christophe Chefd'Hotel, D. Tschumperlé, R. Deriche, O. Faugeras (2004)
Regularizing Flows for Constrained Matrix-Valued ImagesJournal of Mathematical Imaging and Vision, 20
Monowar Bhuyan, D. Bhattacharyya, Hossein Homaei, Hamid Shahriari, Jiankun Hu, Yong Yu, Yi Mu, Guilin Wang, Ying Sun, Hassan Asghar, Josef Pieprzyk, Huaxiong Wang, Jun Zhang, Yang Xiang, Wanlei Zhou, Lei Ye, Lan Zhou, Vijay Varadharajan, M. Hitchens, Lein Harn, Chia-Yin Lee, Changlu Lin, Chin-Chen Chang, Yini Wang, Sheng Wen, Silvio Cesare, Erika Rosas, Olivier Marin, Xavier Bonnaire (1958)
The Computer JournalNature, 181
R. Hardie, T. Tuinstra, Kobus Barnard, J. Bognar, E. Armstrong (1997)
High resolution image reconstruction from digital video with global and non-global scene motionProceedings of International Conference on Image Processing, 1
Zhou Wang, A. Bovik (2002)
A universal image quality indexIEEE Signal Processing Letters, 9
B. Tom, A. Katsaggelos (1994)
Reconstruction of a high-resolution image from multiple-degraded misregistered low-resolution images, 2308
Michael Elad, A. Feuer (1999)
Superresolution restoration of an image sequence: adaptive filtering approachIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 8 3
Sanjeev Kumar, Mainak Biswas, Truong Nguyen (2004)
Global motion estimation in frequency and spatial domain2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 3
J. Conel (1985)
Calibration of AIS Data Using Ground-based Spectral Reflectance Measurements
R. Tsai, Thomas Huang (1984)
Multiframe image restoration and registration
Andrew Patti, M. Sezan, A. Tekalp (1997)
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture timeIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 6 8
H. Stark, P. Oskoui (1989)
High-resolution image recovery from image-plane arrays, using convex projections.Journal of the Optical Society of America. A, Optics and image science, 6 11
S. Kim, N. Bose, H. Valenzuela (1990)
Recursive reconstruction of high resolution image from noisy undersampled multiframesIEEE Trans. Acoust. Speech Signal Process., 38
Sina Farsiu, M. Robinson, Michael Elad, P. Milanfar (2004)
Fast and robust multiframe super resolutionIEEE Transactions on Image Processing, 13
M. Ng, C. Sze, S. Yung (2004)
Wavelet algorithms for deblurring modelsInternational Journal of Imaging Systems and Technology, 14
Seung Kim, Wen-Yu Su (1991)
Recursive high-resolution reconstruction of blurred multiframe images[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing
R. Schultz, R. Stevenson (1994)
A Bayesian approach to image expansion for improved definitioIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 3 3
S. Chaudhuri, J. Manjunath (2005)
Motion-Free Super-Resolution
In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L1 norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.
The Computer Journal – Oxford University Press
Published: Jan 24, 2009
Keywords: Keywords super-resolution MODIS images outliers L 1 norm data fidelity Huber prior
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