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Robust Sparse Analysis Regularization

Samuel Vaiter
November 09, 2011

Robust Sparse Analysis Regularization

Natimages'11, Institut Henri Poincaré, Paris, November 2011

Samuel Vaiter

November 09, 2011
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  1. Inverse Problems ill-posed Linear hypothesis One model y = x0

    + w Observations Operator Unknown signal Noise Several problems Inpaiting Super-resolution Tuesday, November 8, 11
  2. Inverse Problems ill-posed Linear hypothesis One model y = x0

    + w Observations Operator Unknown signal Noise Several problems Inpaiting Super-resolution Regularization x? 2 argmin x 2RN 1 2 || y x ||2 2 + J ( x ) Tuesday, November 8, 11
  3. Inverse Problems ill-posed Linear hypothesis One model y = x0

    + w Observations Operator Unknown signal Noise x? 2 argmin x = y J ( x ) Noiseless 0 Several problems Inpaiting Super-resolution Regularization x? 2 argmin x 2RN 1 2 || y x ||2 2 + J ( x ) Tuesday, November 8, 11
  4. Many almost null coe cients space domain Synthesis Sparsity of

    Natural Images frequency-space domain Orthogonal Wavelets Tuesday, November 8, 11
  5. Many almost null coe cients space domain Good approximation Sparsity

    of natural images in frequency-space domain J(x) = min ↵ || ↵ ||1 subject to x = D↵ Synthesis Sparsity of Natural Images frequency-space domain Orthogonal Wavelets Tuesday, November 8, 11
  6. non-null coe cients : discontinuities Sparsity of cartoon images after

    gradient operator application J ( x ) = || D ⇤ x ||1 Here TV : D⇤ = r An Other Sparsity Measure space domain analysis r Tuesday, November 8, 11
  7. Sparse Regularizations Synthesis argmin ↵2RQ 1 2 ||y ↵||2 2

    + ||↵||1 = D x = D↵ Tuesday, November 8, 11
  8. Sparse Regularizations Synthesis argmin ↵2RQ 1 2 ||y ↵||2 2

    + ||↵||1 = D x = D↵ Analysis argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  9. Sparse Regularizations = 6= 0 D x ↵ Synthesis argmin

    ↵2RQ 1 2 ||y ↵||2 2 + ||↵||1 = D x = D↵ Analysis argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  10. Sparse Regularizations = 6= 0 D x ↵ = D⇤

    x ↵ Synthesis argmin ↵2RQ 1 2 ||y ↵||2 2 + ||↵||1 = D x = D↵ Analysis argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  11. Sparse approx. of x ? in D Sparse Regularizations =

    6= 0 D x ↵ = D⇤ x ↵ Synthesis argmin ↵2RQ 1 2 ||y ↵||2 2 + ||↵||1 = D x = D↵ Analysis argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  12. Correlation of x ? and D sparse Sparse approx. of

    x ? in D Sparse Regularizations = 6= 0 D x ↵ = D⇤ x ↵ Synthesis argmin ↵2RQ 1 2 ||y ↵||2 2 + ||↵||1 = D x = D↵ Analysis argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  13. ||↵||1 = 1 Signal Model of Synthesis Sparsity d1 d2

    y = ↵ ↵? Tuesday, November 8, 11
  14. ||↵||1 = 1 sparsest solution Signal Model of Synthesis Sparsity

    d1 d2 y = ↵ ↵? Tuesday, November 8, 11
  15. Analysis Counterpart d1 d2 d3 || D ⇤ x ||1

    = 1 Tuesday, November 8, 11
  16. Analysis Counterpart d1 d2 d3 || D ⇤ x ||1

    = 1 y = x Tuesday, November 8, 11
  17. Analysis Counterpart d1 d2 d3 || D ⇤ x ||1

    = 1 y = x Tuesday, November 8, 11
  18. Analysis Counterpart d1 d2 d3 || D ⇤ x ||1

    = 1 y = x x ? Tuesday, November 8, 11
  19. Behaviour of Solutions x?( , , D ) = argmin

    x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  20. piecewise a ne y 7! x ? Observations Behaviour of

    Solutions x?( , , D ) = argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 Tuesday, November 8, 11
  21. piecewise a ne y 7! x ? Observations Behaviour of

    Solutions x?( , , D ) = argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 P depends on sign(D ⇤ x ? (y)) and AP is a linear operator If [y, ¯ y] ⇢ P x ?(¯ y ) = AP x ?( y ) u y ¯ y P Tuesday, November 8, 11
  22. piecewise a ne y 7! x ? Observations 7! x

    ? piecewise a ne Scaling Behaviour of Solutions x?( , , D ) = argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 P depends on sign(D ⇤ x ? (y)) and AP is a linear operator If [y, ¯ y] ⇢ P x ?(¯ y ) = AP x ?( y ) u y ¯ y P Tuesday, November 8, 11
  23. piecewise a ne y 7! x ? Observations 7! x

    ? piecewise a ne Scaling D 7! x ? open question Dictionary Behaviour of Solutions x?( , , D ) = argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 P depends on sign(D ⇤ x ? (y)) and AP is a linear operator If [y, ¯ y] ⇢ P x ?(¯ y ) = AP x ?( y ) u y ¯ y P Tuesday, November 8, 11
  24. there exists a unique solution x ? such that ||x?

    x0 ||2 = O(||w||2) supp( D ⇤ x ?) ✓ supp( D ⇤ x0) then, for every x0 such that supp(D ⇤ x0) = I, If RC(I) < 1 and > ||w||2CI, RC ( I ) : function of the support Robustness of Estimators D ⇤ x0 D ⇤ x ? x?( , , D ) = argmin x 2RN 1 2 || y x ||2 2 + || D ⇤ x ||1 y = x0 + w Tuesday, November 8, 11
  25. Joint work with — Gabriel Peyr´ e (CEREMADE, Dauphine) —

    Charles Dossal (IMB, Bordeaux I) — Jalal Fadili (GREYC, ENSICAEN) Any questions ? Robust Sparse Analysis Regularization arXiv:1109.6222 Thanks Tuesday, November 8, 11