A Spectral Approach to Total Variation

The total variation (TV) functional is explored from a spectral perspective. We formulate a TV transform based on the second time derivative of the total variation flow, scaled by time. In the transformation domain disks yield impulse responses. This transformation can be viewed as a spectral domain, with somewhat similar intuition of classical Fourier analysis. A simple reconstruction formula from the TV spectral domain to the spatial domain is given. We can then design low-pass, high-pass and band-pass TV filters and obtain a TV spectrum of signals and images.

[1]  Mila Nikolova,et al.  Regularizing Flows for Constrained Matrix-Valued Images , 2004, Journal of Mathematical Imaging and Vision.

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

[3]  M. Novaga The Total Variation Flow , 2003 .

[4]  Thomas Brox,et al.  A TV flow based local scale estimate and its application to texture discrimination , 2006, J. Vis. Commun. Image Represent..

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

[6]  B. Nadler,et al.  Diffusion maps, spectral clustering and reaction coordinates of dynamical systems , 2005, math/0503445.

[7]  Yehoshua Y. Zeevi,et al.  Variational denoising of partly textured images by spatially varying constraints , 2006, IEEE Transactions on Image Processing.

[8]  Daubechies,et al.  Ten Lectures on Wavelets Volume 921 , 1992 .

[9]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[10]  Ricardo H. Nochetto,et al.  Discrete Total Variation Flows without Regularization , 2012, SIAM J. Numer. Anal..

[11]  Jianhong Shen,et al.  A Good Image Model Eases Restoration , 2002 .

[12]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[13]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[14]  Tony F. Chan,et al.  Aspects of Total Variation Regularized L[sup 1] Function Approximation , 2005, SIAM J. Appl. Math..

[15]  Yehoshua Y. Zeevi,et al.  Estimation of optimal PDE-based denoising in the SNR sense , 2006, IEEE Transactions on Image Processing.

[16]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[17]  S. Osher,et al.  Nonlinear inverse scale space methods , 2006 .

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

[19]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[20]  M. Victor Wickerhauser,et al.  Wavelets: Algorithms and Applications (Yves Meyer) , 1994, SIAM Rev..

[21]  Thomas Brox,et al.  On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs , 2004, SIAM J. Numer. Anal..

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

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

[24]  T. Chan,et al.  Edge-preserving and scale-dependent properties of total variation regularization , 2003 .

[25]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[26]  Bin Luo,et al.  Local Scale Measure from the Topographic Map and Application to Remote Sensing Images , 2009, Multiscale Model. Simul..

[27]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[28]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[29]  Jesús Ildefonso Díaz Díaz,et al.  Some qualitative properties for the total variation flow , 2002 .

[30]  J. Koenderink The structure of images , 2004, Biological Cybernetics.

[31]  Otmar Scherzer,et al.  Inverse Total Variation Flow , 2007, Multiscale Model. Simul..

[32]  Tony F. Chan,et al.  Structure-Texture Image Decomposition—Modeling, Algorithms, and Parameter Selection , 2006, International Journal of Computer Vision.

[33]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  V. Caselles,et al.  Minimizing total variation flow , 2000, Differential and Integral Equations.

[35]  Benjamin Berkels,et al.  Cartoon Extraction Based on Anisotropic Image Classification Vision , Modeling , and Visualization Proceedings , 2006 .

[36]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[37]  B. Porat,et al.  Digital Spectral Analysis with Applications. , 1988 .

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

[39]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[40]  Yoshikazu Giga,et al.  Scale-Invariant Extinction Time Estimates for Some Singular Diffusion Equations , 2010 .

[41]  J. Weickert,et al.  Locally analytic schemes: A link between diffusion filtering and wavelet shrinkage , 2008 .

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