scholarly journals Robust sparse image reconstruction of radio interferometric observations with purify

2017 ◽  
Vol 473 (1) ◽  
pp. 1038-1058 ◽  
Author(s):  
Luke Pratley ◽  
Jason D. McEwen ◽  
Mayeul d'Avezac ◽  
Rafael E. Carrillo ◽  
Alexandru Onose ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Sparse Image Reconstruction

scholarly journals LOFAR sparse image reconstruction

2015 ◽  
Vol 575 ◽  
pp. A90 ◽  
Author(s):  
H. Garsden ◽  
J. N. Girard ◽  
J. L. Starck ◽  
S. Corbel ◽  
C. Tasse ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Sparse Image Reconstruction

scholarly journals Sparse Image Reconstruction for Contrast Enhanced Cardiac Ultrasound using Diverging Waves

2019 ◽  
Author(s):  
Antonio Stanziola ◽  
Matthieu Toulemonde ◽  
Virginie Papadopoulou ◽  
Richard Corbett ◽  
Neill Duncan ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Cardiac Ultrasound ◽  
Contrast Enhanced ◽  
Sparse Image Reconstruction

Multi-Angle SAR Sparse Image Reconstruction With Improved Attributed Scattering Model

2020 ◽  
Vol 17 (7) ◽  
pp. 1188-1192
Author(s):  
Yangkai Wei ◽  
Yinchuan Li ◽  
Xinliang Chen ◽  
Zegang Ding
Keyword(s):  
Image Reconstruction ◽  
Scattering Model ◽  
Sparse Image Reconstruction

High-efficiency imaging through scattering media in noisy environments via sparse image reconstruction

2015 ◽  
Vol 54 (11) ◽  
pp. 113111 ◽  
Author(s):  
Tengfei Wu ◽  
Xiaopeng Shao ◽  
Changmei Gong ◽  
Huijuan Li ◽  
Jietao Liu
Keyword(s):  
Image Reconstruction ◽  
High Efficiency ◽  
Noisy Environments ◽  
Scattering Media ◽  
Sparse Image Reconstruction

Challenges in Sparse Image Reconstruction

2020 ◽  
pp. 2150026
Author(s):  
S. Shashi Kiran ◽  
K. V. Suresh
Keyword(s):  
Image Processing ◽  
Image Reconstruction ◽  
Sparse Representation ◽  
Satellite Image ◽  
Super Resolution ◽  
Learning Approach ◽  
Challenges And Opportunities ◽  
Processing Signal ◽  
Satellite Image Processing ◽  
Sparse Image Reconstruction

Handling huge amount of data from different sources more so in the images is the latest challenge. One of the solutions to this is sparse representation. The idea of sparsity has been receiving much attention recently from many researchers in the areas such as satellite image processing, signal processing, medical image processing, microscopy image processing, pattern recognition, neuroscience, seismic imaging, etc. Many algorithms have been developed for various areas of sparse representation. The main objective of this paper is to provide a comprehensive study and highlight the challenges in the area of sparse representation which will be helpful for researchers. Also, the current challenges and opportunities of applying sparsity to image reconstruction, namely, image super-resolution, image denoising and image restoration are discussed. This survey on sparse representation categorizes the existing methods into three groups: dictionary learning approach, greedy strategy approximation approach and deep learning approach.

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