压缩感知与单像素成像

Single pixel camera (Compressive ghost imaging)
4 . Application
Initial image
Reconstruction
filtering
compression ratio: 7.93% PSNR： 62.048 dB
4 . Application
Zhexuan Tao
Emmanuel Candès
2 . Principle
Y(m*1)
Φ(m*n)
X(n*1) k sparseness
Ψ(n*n)
S(n*1)
Where m satisfied the equation m ≥ C*k*log(n/k) (C is a constant) Usually, Ψ is a known basis, knowledge of X is equivalent to knowledge of S. Y=ΦΨS=ΦX How to solve X from the function Y=ΦX. That is a convex optimization problem
2 . Principle
Generally compressive sensing relied on the assumption that the solution to the l1 minimization problem provides the correct solution and is computationally feasible. However work has been done to find alternative algorithms that are faster or give superior reconstruction performance. Matching Pursuit (MP) Orthogonal Matching Pursuit (OMP) Stagewise Orthogonal Matching Pursuit (StOMP) Compressive Sampling Matching Pursuit (CoSaMP) Two-Step Iterative Shrinkage Thresholding (TwIST)
| Compressive Sensing of Images
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wenku.baidu.com
晨
孙晓雯 李显业
1 . Introduction
Signal
Sensing Detector
Compressing Computer
Saving Storage
Signal
Sensing Compressing
Detector
Saving Storage
4 . Application
Thanks
2 . Principle
sparse transform FFT DCT DWT …… Initial image Inverse transformation
Sparse image
Measurement matrix
Reconstruction algorithms
Sparse image
Measurement matrix
compressed image
3 . Simulation
Initial image
DWT
Sparse
Measurement
Reconstruction
compression ratio (OMP) DWT
30% DCT
CC
PSNR DCT Sparse Reconstruction
0.9327
23.2955
0.8825
20.8395
4 . Application
Single pixel camera (computational ghost imaging) Y=ΦX
4 . Application
BD Laser Expander SLM Object
Y=ΦΨS=ΦX
Computer