IMAGE RECOVERY USING FUNCTIONS OF BOUNDED VARIATION AND SOBOLEV SPACES OF NEGATIVE DIFFERENTIABILITY

In this work we wish to recover an unknown image from a blurry, or noisy-blurry version. We solve this inverse problem by energy minimization and regularization. We seek a solution of the form $u + v$, where $u$ is a function of bounded variation (cartoon component), while $v$ is an oscillatory component (texture), modeled by a Sobolev function with negative degree of differentiability. We give several results of existence and characterization of minimizers of the proposed optimization problem. Experimental results show that this cartoon + texture model better recovers textured details in natural images, by comparison with the more standard models where the unknown is restricted only to the space of functions of bounded variation.

[1]  A. Tikhonov On the stability of inverse problems , 1943 .

[2]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .

[3]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  G. F. Roach,et al.  Inverse problems and imaging , 1991 .

[5]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[6]  Stanley Osher,et al.  Total variation based image restoration with free local constraints , 1994, Proceedings of 1st International Conference on Image Processing.

[7]  C. Vogel,et al.  Analysis of bounded variation penalty methods for ill-posed problems , 1994 .

[8]  L. Vese,et al.  A Variational Method in Image Recovery , 1997 .

[9]  P. Lions,et al.  Image recovery via total variation minimization and related problems , 1997 .

[10]  Yann Gousseau,et al.  Scales in Natural Images and a Consequence on their Bounded Variation Norm , 1999, Scale-Space.

[11]  Luis Álvarez,et al.  The Size of Objects in Natural and Artificial Images , 1999 .

[12]  S. Durand,et al.  IMAGE DEBLURRING, SPECTRUM INTERPOLATION AND APPLICATION TO SATELLITE IMAGING , 2000 .

[13]  D. Mumford,et al.  Stochastic models for generic images , 2001 .

[14]  Yves Meyer,et al.  Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures , 2001 .

[15]  Yann Gousseau,et al.  Are Natural Images of Bounded Variation? , 2001, SIAM J. Math. Anal..

[16]  L. Vese A Study in the BV Space of a Denoising—Deblurring Variational Problem , 2001 .

[17]  S. Osher,et al.  IMAGE DECOMPOSITION AND RESTORATION USING TOTAL VARIATION MINIMIZATION AND THE H−1 NORM∗ , 2002 .

[18]  François Malgouyres,et al.  Minimizing the total variation under a general convex constraint for image restoration , 2002, IEEE Trans. Image Process..

[19]  François Malgouyres A Framework for Image Deblurring Using Wavelet Packet Bases , 2002 .

[20]  Stanley Osher,et al.  Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing , 2003, J. Sci. Comput..

[21]  Stanley Osher,et al.  Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..

[22]  S. Lintner,et al.  Solving a variational image restoration model which involves L∞ constraints , 2004 .

[23]  Eitan Tadmor,et al.  A Multiscale Image Representation Using Hierarchical (BV, L2 ) Decompositions , 2004, Multiscale Model. Simul..

[24]  V. Caselles,et al.  Parabolic Quasilinear Equations Min-imizing Linear Growth Functionals , 2004 .

[25]  Luminita A. Vese,et al.  Image Decomposition Using Total Variation and div(BMO) , 2005, Multiscale Model. Simul..

[26]  Antonin Chambolle,et al.  Dual Norms and Image Decomposition Models , 2005, International Journal of Computer Vision.

[27]  Stacey Levine,et al.  An Adaptive Variational Model for Image Decomposition , 2005, EMMCVPR.

[28]  I. Daubechiesa,et al.  Variational image restoration by means of wavelets : Simultaneous decomposition , deblurring , and denoising , 2005 .

[29]  G. Aubert,et al.  Modeling Very Oscillating Signals. Application to Image Processing , 2005 .

[30]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[31]  Antonin Chambolle,et al.  Image Decomposition into a Bounded Variation Component and an Oscillating Component , 2005, Journal of Mathematical Imaging and Vision.

[32]  Nahum Kiryati,et al.  Semi-blind image restoration via Mumford-Shah regularization , 2006, IEEE Transactions on Image Processing.

[33]  Mila Nikolova,et al.  Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image Restoration , 2005, SIAM J. Sci. Comput..

[34]  Nahum Kiryati,et al.  Image Deblurring in the Presence of Impulsive Noise , 2006, International Journal of Computer Vision.

[35]  Y. Meyer,et al.  Image decompositions using bounded variation and generalized homogeneous Besov spaces , 2007 .

[36]  I. Daubechies,et al.  Iteratively solving linear inverse problems under general convex constraints , 2007 .

[37]  Peyman Milanfar,et al.  Deblurring Using Regularized Locally Adaptive Kernel Regression , 2008, IEEE Transactions on Image Processing.

[38]  L. Vese,et al.  MULTISCALE HIERARCHICAL DECOMPOSITION OF IMAGES WITH APPLICATIONS TO DEBLURRING, DENOISING AND SEGMENTATION ∗ , 2008 .

[39]  L. Lieu,et al.  Image Restoration and Decomposition via Bounded Total Variation and Negative Hilbert-Sobolev Spaces , 2008 .

[40]  Yunho Kim,et al.  Functional minimization problems in image processing , 2008, Electronic Imaging.

[41]  Luminita A. Vese,et al.  Modeling Oscillatory Components with The Homogeneous Spaces BM -α and W -α,p* , 2011 .