Complex SAR Image Compression Using Entropy-Constrained Dictionary Learning and Universal Trellis Coded Quantization

2016 ◽  
Vol 25 (4) ◽  
pp. 686-691 ◽  
Author(s):  
Xin Zhan ◽  
Rong Zhang
Keyword(s):  
Image Compression ◽  
Dictionary Learning ◽  
Sar Image ◽  
Trellis Coded

Real-time In-network Image Compression via Distributed Dictionary Learning

2021 ◽  
pp. 1-1
Author(s):  
Parul Pandey ◽  
Mehdi Rahmati ◽  
Waheed U. Bajwa ◽  
Dario Pompili
Keyword(s):  
Image Compression ◽  
Real Time ◽  
Dictionary Learning

Spectral Dictionary Learning Based Multispectral Image Compression

2019 ◽  
pp. 140-144
Author(s):  
Wei Liang ◽  
Yinghui Wang ◽  
Wen Hao ◽  
Xiuxiu Li ◽  
Xiuhong Yang ◽  
...  
Keyword(s):  
Image Compression ◽  
Dictionary Learning ◽  
Multispectral Image ◽  
Multispectral Image Compression

Effects of dictionary scale on dictionary learning for DICOM image compression

2015 ◽  
Vol 30 (6) ◽  
pp. 1045-1051
Author(s):  
酉 霞 YOU Xia ◽  
陈 菲 CHEN Fei ◽  
贾小林 JIA Xiao-lin ◽  
刘雨娇 LIU Yu-jiao ◽  
杨 勇 YANG Yong
Keyword(s):  
Image Compression ◽  
Dictionary Learning ◽  
Dicom Image

A Novel CS Model and Its Application in Complex SAR Image Compression

2013 ◽  
Vol E96.A (11) ◽  
pp. 2209-2217
Author(s):  
Wentao LV ◽  
Gaohuan LV ◽  
Junfeng WANG ◽  
Wenxian YU
Keyword(s):  
Image Compression ◽  
Sar Image

scholarly journals Product Dictionary Learning-Based SAR Target Configuration Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Junsheng Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Product Model ◽  
Sar Images ◽  
Stationary Target ◽  
Target Configuration ◽  
Key Factor

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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