Recursive Risk Estimation For Non-Linear Image Deconvolution With A Wavelet-Domain Sparsity Constraint

We propose a recursive data-driven risk-estimation method for non-linear iterative deconvolution. Our two main contributions are 1) a solution-domain risk-estimation approach that is applicable to non-linear restoration algorithms for ill-conditioned inverse problems; and 2) a risk estimate for a state-of-the-art iterative procedure, the thresholded Landweber iteration, which enforces a wavelet-domain sparsity constraint. Our method can be used to estimate the SNR improvement at every step of the algorithm; e.g., for stopping the iteration after the highest value is reached. It can also be applied to estimate the optimal threshold level for a given number of iterations.


Published in:
2008 15Th Ieee International Conference On Image Processing, Vols 1-5, 665-668
Presented at:
15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, CA, Oct 12-15, 2008
Year:
2008
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
ISBN:
978-1-4244-1765-0
Keywords:
Laboratories:




 Record created 2012-06-12, last modified 2018-03-17

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