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Image Reconstruction based on Back-Propagation Learning in Compressed Sensing theory

Image Reconstruction based on Back-Propagation Learning in Compressed Sensing theory. ECE 539 Intro-ANN Gaoang Wang. 1. Compressed Sensing Theory. Since y =Φ x , x =Ψ f, then y= Φ x= ΦΨ f

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Image Reconstruction based on Back-Propagation Learning in Compressed Sensing theory

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  1. 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

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

  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.

  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.

  6. 5. results

  7. 5. results

  8. Thank you !

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