Total variation improved wavelet thresholding in image compression

In this paper, we propose using partial differential equation (PDE) techniques in wavelet based image processing to reduce edge artifacts generated by wavelet thresholding. We employ minimization techniques, in particular the minimization of total variation, to modify the retained standard wavelet coefficients so that the reconstructed images have less oscillations near edges. Numerical experiments show that this approach improves the reconstructed image quality in wavelet compression and in denoising.

[1]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[2]  R. L. Claypoole,et al.  Nonlinear wavelet transforms for image coding , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[3]  Antonin Chambolle,et al.  Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage , 1998, IEEE Trans. Image Process..

[4]  Gene H. Golub,et al.  A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration , 1999, SIAM J. Sci. Comput..

[5]  Curtis R. Vogel,et al.  Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..

[6]  Haomin Zhou,et al.  Adaptive ENO-wavelet transforms for discontinuous functions , 2001 .

[7]  Tony F. Chan,et al.  Feature preserving lossy image compression using nonlinear PDEs , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[8]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

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

[10]  Tony F. Chan,et al.  Spatially adaptive local-feature-driven total variation minimizing image restoration , 1997, Optics & Photonics.

[11]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[12]  ProblemsPer Christian HansenDepartment The L-curve and its use in the numerical treatment of inverse problems , 2000 .

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