1. ECE 539 Intro-ANN Gaoang Wang
Image Reconstruction based on Back-Propagation Learning in Compressed Sensing theory
ECE 539 Intro-ANN
Gaoang Wang

2. 1. Compressed Sensing Theory
Since y=Φx, x=Ψf, then y=Φx=ΦΨf
Where x is original signal, y is sampled signal, Φ (m by n) is measurement matrix with m<<n, and Ψ is transform matrix
UW-Madison

3. 2. SPL-BCS (by James E. Fowler)
UW-Madison

4. 3. Learning Method in Sampling
Feature vector (64 by 1): from one patch (8 by 8) of sampled image. pinv(mea_matrix)*mea_matrix*patch 5 Input Images (256 by 256) have 5120 patches in total as training data. Using back-propagation learning method to derive the weights of classification.
UW-Madison

5. 4. Modified Sampling Method
First sampling: sampling input images in general and using pre-generating weights to decide which part of the original image can bear a higher compression ratio. Second sampling: transform the satisfied patches into lower dimension.
UW-Madison

6. 5. results Image C_Rate C_Ratio PSNR BCS Ratio Lena 0.8572 14.2510 8
Barbara
0.7781
Goldhill
0.7739
Baboon
0.6860
Peppers
0.8831
test1
0.8921
test2
0.8701
test3
0.7905
test4
0.8435
UW-Madison

7. 5. results
UW-Madison

8. Thank you !
UW-Madison