A texture model based on a concentration of measure

Hayden Schaeffer; John Garnett; Luminita A. Vese

doi:10.3934/ipi.2013.7.927

Article Contents

2013, Volume 7, Issue 3: 927-946. Doi: 10.3934/ipi.2013.7.927

This issue Previous Article Statistical ranking using the $l^{1}$-norm on graphs Next Article 3D adaptive finite element method for a phase field model for the moving contact line problems

A texture model based on a concentration of measure

1.
Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095
2.
Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095-1555

Received: July 2012

Revised: May 2013

Published: August 2013

Abstract / Introduction Related Papers Cited by

Abstract

Cartoon-texture regularization is a technique for reconstructing fine-scale details from ill-posed imaging problems, which are often ill-posed because of some non-invertible convolution kernel. Here we propose a cartoon-texture regularization for deblurring problems with semi-known kernel. The cartoon component is modeled by a function of bounded variation, while the texture component is measured by an approximate duality with Lipschitz functions ($W^{1,\infty}$). To approximate the dual Lipschitz norm, which is difficult to calculate, we propose an approach using concentration of measure. This provides an accurate and differentiable expression. We also present numerical results for our cartoon-texture decomposition, both in the case of a semi-known deblurring kernel and the case of a known kernel. The texture norm enables one to numerically reconstruct fine scale details that are typically difficult to recover from blurred images, and for semi-known deblurring the method quickly leads to the correct kernel, even when that kernel contains noise.

Keywords:

Mathematics Subject Classification: 49M27, 28E99, 35K65.

Citation:

Related Papers

Cited by

References

[1]	G. Aubert and P. Kornprobst, "Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variations," Second edition, Applied Mathematical Sciences, 147, Springer, New York, 2006.
[2]	Gilles Aubert and Jean-François Aujol, Modeling very oscillating signals. Application to image processing, Applied Mathematics and Optimization, 51 (2005), 163-182.doi: 10.1007/s00245-004-0812-z.
[3]	J.-F. Aujol, "Contribution à l'Analyse de Textures en Traitement d'Images par Méthodes Variationnelles et Équations aux Dérivées Partielles," Ph.D thesis, June, 2004.
[4]	J.-F. Aujol and T. F. Chan, Combining geometrical and textured information to perform image classification, Journal of Visual Communication and Image Representation, 17 (2006), 1004-1023.doi: 10.1016/j.jvcir.2006.02.001.
[5]	J.-F. Aujol and S. H. Kang, Color image decomposition and restoration, Journal of Visual Communication and Image Representation, 17 (2006), 916-928.doi: 10.1016/j.jvcir.2005.02.001.
[6]	Jean-François Aujol, Gilles Aubert, Laure Blanc-Féraud and Antonin Chambolle, Image decomposition into a bounded variation component and an oscillating component, Journal of Mathematical Imaging and Vision, 22 (2005), 71-88.doi: 10.1007/s10851-005-4783-8.
[7]	L. Bar, N. Sochen and N. Kiryati, Semi-blind image restoration via Mumford-Shah regularization, Image Processing, IEEE Transactions on, 15 (2006), 483-493.doi: 10.1109/TIP.2005.863120.
[8]	M. Bertalmio, L. Vese, G. Sapiro and S. Osher, Simultaneous structure and texture image inpainting, IEEE Transactions on Image Processing, 12 (2003), 882-889.
[9]	M. G. Crandall, L. C. Evans and R. F. Gariepy, Optimal Lipschitz extensions and the infinity Laplacian, Calculus of Variations and Partial Differential Equations, 13 (2001), 123-139.
[10]	I. Daubechies and G. Teschke, Variational image restoration by means of wavelets: Simultaneous decomposition, deblurring, and denoising, Applied and Computational Harmonic Analysis, 19 (2005), 1-16.doi: 10.1016/j.acha.2004.12.004.
[11]	I. Ekeland and R. Témam, "Convex Analysis and Variational Problems," SIAM, 1999.
[12]	L. C. Evans and Y. Yu, Various properties of solutions of the infinity-Laplacian equation, Communications in Partial Differential Equations, 30 (2005), 1401-1428.doi: 10.1080/03605300500258956.
[13]	J. B. Garnett, T. M. Le, Y. Meyer and L. A. Vese, Image decompositions using bounded variation and generalized homogeneous besov spaces, Applied and Computational Harmonic Analysis, 23 (2007), 25-56.doi: 10.1016/j.acha.2007.01.005.
[14]	G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Modeling & Simulation, 7 (2009), 1005-1028.doi: 10.1137/070698592.
[15]	J. Gilles, Noisy image decomposition: A new structure, texture and noise model based on local adaptivity, Journal of Mathematical Imaging and Vision, 28 (2007), 285-295.doi: 10.1007/s10851-007-0020-y.
[16]	J. Gilles and Y. Meyer, Properties of BV-G structures + textures decomposition models. Application to road detection in satellite images, IEEE Transactions on Image Processing}, 19 (2010), 2793-2800.doi: 10.1109/TIP.2010.2049946.
[17]	Y. Kim and L. Vese, Image recovery using functions of bounded variation and sobolev spaces of negative differentiability, Inverse Problems and Imaging, 3 (2009), 43-68.doi: 10.3934/ipi.2009.3.43.
[18]	T. M. Le, L. H. Lieu and L. A. Vese, $(\phi,\phi)$ image decomposition models and minimization algorithms,* Journal of Mathematical Imaging and Vision, 33 (2009), 135-148.doi: 10.1007/s10851-008-0130-1.
[19]	T. M. Le and L. A. Vese, Image decomposition using total variation and div (bmo), Multiscale Modeling and Simulation, 4 (2005), 390-423.doi: 10.1137/040610052.
[20]	L. H. Lieu and L. A. Vese, Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces, Applied Mathematics and Optimization, 58 (2008), 167-193.doi: 10.1007/s00245-008-9047-8.
[21]	G. Lu and P. Wang, Inhomogeneous infinity Laplace equation, Advances in Mathematics, 217 (2008), 1838-1868.doi: 10.1016/j.aim.2007.11.020.
[22]	Y. Meyer, "Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures," University Lecture Series, 22, American Mathematical Soc., Providence, RI, 2001.
[23]	D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, 42 (1989), 577-685.doi: 10.1002/cpa.3160420503.
[24]	S. Osher, A. Solé and L. Vese, Image decomposition and restoration using total variation minimization and the h1, Multiscale Modeling & Simulation, 1 (2003), 349-370.doi: 10.1137/S1540345902416247.
[25]	R. J. Renka, A simple explanation of the sobolev gradient method, (2006).
[26]	W. B. Richardson, Sobolev gradient preconditioning for image-processing PDEs, Communications in Numerical Methods in Engineering, 24 (2006), 493-504.doi: 10.1002/cnm.951.
[27]	L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268.doi: 10.1016/0167-2789(92)90242-F.
[28]	Hayden Schaeffer and Stanley Osher, A low patch-rank interpretation of texture, SIAM Journal on Imaging Sciences, 6 (2013), 226-262.doi: 10.1137/110854989.
[29]	J. Shen, Piecewise $H^{-1} -H^0 - H^1$ images and the Mumford-Shah-Sobolev model for segmented image decomposition, APPL. MATH. RES. EXP, 4 (2005), 2005.
[30]	G. Sundaramoorthi, A. Yezzi and A. C. Mennucci, Sobolev active contours, International Journal of Computer Vision, 73 (2007), 345-366.
[31]	L. A. Vese and S. J. Osher, Modeling textures with total variation minimization and oscillating patterns in image processing, Journal of Scientific Computing, 19 (2002), 553-572.doi: 10.1023/A:1025384832106.
[32]	L. A. Vese and S. J. Osher, Image denoising and decomposition with total variation minimization and oscillatory functions, Journal of Mathematical Imaging and Vision, 20 (2004), 7-18.doi: 10.1023/B:JMIV.0000011316.54027.6a.