Joint compressed sensing and JPEG coding based secure compression scheme in OFDM-PON

2022 ◽  
pp. 127901
Author(s):  
Yuhang Chen ◽  
Chongfu Zhang ◽  
Mengwei Cui ◽  
Yufeng Luo ◽  
Tingwei Wu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Compression Scheme

scholarly journals Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Wei Jin ◽  
Zhen Liu ◽  
Gang Li
Keyword(s):  
Compressed Sensing ◽  
High Performance ◽  
Filter Banks ◽  
Neutron Radiation ◽  
Sampling Theorem ◽  
Original Image ◽  
Compression Scheme ◽  
Directional Filter ◽  
Directional Filter Banks ◽  
Block Based

An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS), which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB) is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.

Neue "Zeiten" in der MRT-Wirbelsäulendiagnostik: Compressed Sensing und Turbo spin echo prescans zur beschleunigten Bildgebung

2020 ◽  
Author(s):  
G Bratke ◽  
C Kabbasch ◽  
D Maintz ◽  
R Rau ◽  
S Haneder ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spin Echo ◽  
Turbo Spin Echo ◽  
Turbo Spin

Compressed Sensing MRI using Curvelet Sparsity and Nonlocal Total Variation: CS-NLTV

2017 ◽  
Vol 2017 (13) ◽  
pp. 5-9 ◽  
Author(s):  
Ali Pour Yazdanpanah ◽  
Emma E. Regentova
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Nonlocal Total Variation
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