scholarly journals Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

2011 ◽  
Vol 20 (7) ◽  
pp. 1838-1857 ◽  
Author(s):  
Weisheng Dong ◽  
Lei Zhang ◽  
Guangming Shi ◽  
Xiaolin Wu
Keyword(s):  
Super Resolution ◽  
Image Deblurring ◽  
Adaptive Regularization ◽  
Domain Selection

scholarly journals PET Image Deblurring and Super-Resolution With an MR-Based Joint Entropy Prior

2019 ◽  
Vol 5 (4) ◽  
pp. 530-539 ◽  
Author(s):  
Tzu-An Song ◽  
Fan Yang ◽  
Samadrita Roy Chowdhury ◽  
Kyungsang Kim ◽  
Keith A. Johnson ◽  
...  
Keyword(s):  
Super Resolution ◽  
Image Deblurring ◽  
Joint Entropy

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 584-597 ◽  
Author(s):  
Sébastien Tourbier ◽  
Xavier Bresson ◽  
Patric Hagmann ◽  
Jean-Philippe Thiran ◽  
Reto Meuli ◽  
...  
Keyword(s):  
Total Variation ◽  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Adaptive Regularization

scholarly journals Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

2022 ◽  
Author(s):  
Haoxuan Yuan ◽  
Rahat Ihsan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Super Resolution ◽  
Weather Radar ◽  
Reconstruction Algorithm ◽  
Radar Data ◽  
Structure Information ◽  
Radar Echo ◽  
Domain Selection

Abstract Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

Real-Time Image Deblurring and Super Resolution Using Convolutional Neural Networks

2021 ◽  
pp. 381-394
Author(s):  
Nidhi Galgali ◽  
Melita Maria Pereira ◽  
N. K. Likitha ◽  
B. R. Madhushri ◽  
E. S. Vani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Deblurring ◽  
Real Time Image ◽  
Time Image

Image deblurring and super resolution using bilateral filter and sparse representation

2020 ◽  
Vol 33 ◽  
pp. 3922-3929
Author(s):  
Jai Iyer ◽  
E. Chitra ◽  
Vivek Maik ◽  
Suparn Padhi ◽  
Sarthak Gupta ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Bilateral Filter ◽  
Image Deblurring
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