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Super-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms
2012
Information Fusion
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This work considers stochastic regularized multi-frame SR image reconstruction from the data-fidelity point of view. ...
In multi-frame Super-Resolution (SR) image reconstruction a single High-Resolution (HR) image is created from a sequence of Low-Resolution (LR) frames. ...
Anastassopoulos, "Super-Resolution Image Reconstruction Techniques: Trade-Offs Between the Data-fidelity and Regularization Terms", Information Fusion, doi:10.1016 /j.inffus.2010 .11.005, 2010 J. ...
doi:10.1016/j.inffus.2010.11.005
fatcat:c2koxj3albfddmyfvflnaskrjq
Diffusion-Steered Super-Resolution Image Reconstruction
[chapter]
2018
Colorimetry and Image Processing
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For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. ...
The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate ...
[18] , f = uη, where f is the corrupted image and λ is the fidelity parameter that balances the trade-off between u and f. ...
doi:10.5772/intechopen.71024
fatcat:yogrp3q4j5h35osvb3z3r7px4i
Adaptive Super-Resolution Image Reconstruction with Lorentzian Error Norm
2017
Indian Journal of Science and Technology
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Super resolution reconstruction(SRR) is a computational technique to correct the degradation that the captured images normally suffer and this problem is ill-posed due to blur and noise present in the ...
Objectives: To focus on an inverse problem of reconstructing a high resolution image from set of captured low resolution (LR) frames. ...
Estimation of λ based on U-Curve Method The SRR problem depends on trade-off between the data fidelity term and prior term, and is totally controlled by the λ. ...
doi:10.17485/ijst/2017/v10i16/106780
fatcat:h7ubthev4bhkjjs62pcp4tedgy
POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter
[article]
2011
arXiv
pre-print
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Super-resolution enables the extraction of this information by reconstructing a single image, at a high resolution than is present in any of the individual images. ...
Super-resolution image restoration has been one of the most important research areas in recent years which goals to obtain a high resolution (HR) image from several low resolutions (LR) blurred, noisy, ...
The projection of an arbitrary
Another important issue is the proper choice of . is regularization parameter that controls a trade-off between the fidelity to data (expressed by ||Y-HX|| 2 ...
arXiv:1112.1484v1
fatcat:xxyatqceurazvmy43twtpcjqni
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
[article]
2018
arXiv
pre-print
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Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. ...
Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. ...
R(x) ∝ − log p(x) denotes a regularization term with the regularization weight λ ≥ 0 to weight this term relative to the data fidelity. ...
arXiv:1812.09375v1
fatcat:k6gfmgb4o5cxvmaeoay4zfb5ly
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
[article]
2021
arXiv
pre-print
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There are mainly two strategies dealing with the speed-resolution trade-off: (1) k-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and ...
In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recovering HR images from low-resolution under-sampled k-space data directly. ...
This work serves as a preliminary attempt to bridge the gap between MR image reconstruction and super-resolution. ...
arXiv:2104.05901v1
fatcat:h2ibk6ohfvg3zjyr4chstenytu
Image Super-Resolution Based on Alternative Registration, Blur Identification and Reconstruction
[chapter]
2012
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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A solution to the problem of obtaining a high-resolution image from several low-resolution images is provided. ...
The cost function is alternatively minimized with respect to registration parameters, blurring operators, and high resolution image. ...
Adaptive Regularization and Step Size The regularization parameter, λ X, B, p , controls the trade-off between fidelity to the data and smoothness of the solution. ...
doi:10.1007/978-3-642-32573-1_8
fatcat:artbx3tba5gh7dfbcksdkgowqu
Single image super-resolution reconstruction based on genetic algorithm and regularization prior model
2016
Information Sciences
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Stolkin, R 2016, 'Single image super-resolution reconstruction based on genetic algorithm and regularization prior model ', Information Sciences, vol. 372, ...
(7) , I is the identity matrix, and the first 2 l -norm term is the fidelity term; the second term is the non-local similarity regularization term, and is the trade-off parameter to balance the non-local ...
As one of the basic techniques in image reconstruction, super-resolution [21] reconstruction aims to derive a high resolution image from one or more low resolution image frames. ...
doi:10.1016/j.ins.2016.08.049
fatcat:kds4pxkixbhtnkqe2l4h5dp5bq
Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging
[chapter]
2014
Lecture Notes in Computer Science
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Our guided super-resolution algorithm is formulated as joint maximum a-posteriori estimation to reconstruct high-resolution range and photometric data. ...
In this paper, we augment multi-frame super-resolution with the concept of guided filtering for simultaneous upsampling of 3-D range data and complementary photometric information in hybrid range imaging ...
and the support by the DFG under Grant No. ...
doi:10.1007/978-3-319-11752-2_18
fatcat:desajgi5xfggzhmwm72aluqyri
A MAP Algorithm to Super-Resolution Image Reconstruction
Third International Conference on Image and Graphics (ICIG'04)
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Super-resolution image reconstruction has been one of the most active research areas in recent years. ...
In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different ...
the balance between fidelity to the data and smoothness of the solution [11] . ...
doi:10.1109/icig.2004.8
dblp:conf/icig/ShenLZZ04
fatcat:nmfdoalx25gtvmtlghecwl25ny
A comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows
[article]
2021
arXiv
pre-print
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Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. ...
This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstruct ...
Raj Kumar Singh who provided expertise which greatly assisted us in the research and improved the paper significantly. ...
arXiv:2107.03361v1
fatcat:okltzsj6uzer3em2hcectjyxzq
Image and video quality improvement techniques for emerging applications
2012
EURASIP Journal on Advances in Signal Processing
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Another objective is evaluation of the trade-off between AL-FEC redundancy and video quality degradation for a given packet loss ratio. ...
A post-processing synchronization between the original and the reconstructed streams has also been designed for improving the fidelity of the quality measures. ...
Phillip Regalia, and the entire Publication Staff at Hindawi and BioMed for their dedication and hard work. ...
doi:10.1186/1687-6180-2012-33
fatcat:teei3npauzgjhnwauovkwlo5xa
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
[article]
2021
arXiv
pre-print
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techniques used in image super-resolution. ...
Our model efficiently reconstructs the high-fidelity DNS data from the LES like low-resolution solutions while yielding good reconstruction metrics. ...
Herein lies the inherent trade-off between solution fidelity and computational complexity that CFD researchers and practitioners constantly grapple with. ...
arXiv:2010.11348v2
fatcat:may6m754qrcbto3nr3jgzg732i
Techniques for flexible image/video resolution conversion with heterogeneous terminals
2007
IEEE Communications Magazine
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The first term, which is the same as the traditional cost function, is the data fidelity penalty term which measures the similarity between the estimated HR frame and LR frames. ...
The trade-off would be processing com-IFigure 3. ...
doi:10.1109/mcom.2007.284539
fatcat:73zhf6f2zfdorbp7stvgqlvbwa
Robust multi-frame super-resolution with adaptive norm choice and difference curvature based BTV regularization
2017
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
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Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. ...
Since super-resolution is an ill-posted problem, regularization techniques are widely used to constrain the minimization function. ...
K is the total number of LR images. Υ(X) is the regularization term. λ is the trade-off parameter between the fidelity term and the regularization term. ...
doi:10.1109/globalsip.2017.8308670
dblp:conf/globalsip/LiuZ17
fatcat:he6t4k4nzzerpfh4qpopma3mai
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