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Cited by 93Advanced Image Processing TechniquesImage and Signal Denoising MethodsImage Processing Techniques and Applications

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

Faramarzi, E., Rajan, D., Christensen, M. P.·2013·IEEE Transactions on Image Processing
DOI 10.1109/tip.2013.2237915
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Abstract

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life i…

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Research works printed but not linked: 42

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Cited by

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Metadata

Authors
Faramarzi, E., Rajan, D., & Christensen, M. P.
Affiliations
Southern Methodist University (US)
ORCID
Venue
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-06
Volume
22
Issue
6
Pages
2101-2114
ISSN
  • 1057-7149 (ISSN-L)
  • 1941-0042
DOI
10.1109/tip.2013.2237915
OpenAlex
W2117998419
Cited by
93
References
3
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 5 · 2020: 9 · 2019: 7 · 2018: 8 · 2017: 9 · 2016: 7 · 2015: 8 · 2014: 11 · 2013: 5
FWCI
11.62
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Image Processing (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
  • Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
Keywords
multi-frame super-resolution, blind image restoration, alternating optimization, image registration, computational imaging, low-resolution images
MeSH terms
Algorithms, Animals, Computer Simulation, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Markov Chains, Photography, Phantoms, Imaging

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CoChat. (2026). Citation check: Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution. https://app.cochat.ai/papers/10.1109/tip.2013.2237915
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Faramarzi, E., Rajan, D., & Christensen, M. P. (2013). Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2013.2237915

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