Abstract
Nonlinear diffusion, proposed by Perona-Malik, is a well-known method for image denoising with edge preserving characteristics. Recently, nonlinear diffusion has been shown to be equivalent to iterative wavelet shrinkage, but only for (1) Mallat-Zhong dyadic wavelet transform and (2) Haar wavelet transform. In this paper, we generalize the equivalence of nonlinear diffusion to non-linear shrinkage in the standard discrete wavelet transform (DWT) domain. Two of the major advantages of the standard DWT are its simplicity (as compared to 1) and its potential to benefit from a greater range of orthogonal and biorthogonal filters (as compared to both 1 and 2). We also extend the wavelet diffusion implementation to multiscale. The qualitative and quantitative results shown for a variety of images contaminated with noise demonstrate the promise of the proposed standard wavelet diffusion.
| Original language | English |
|---|---|
| Pages (from-to) | 20-28 |
| Number of pages | 9 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 5099 LNCS |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 3rd International Conference on Image and Signal Processing, ICISP 2008 - Cherbourg-Octeville, France Duration: 1 Jul 2008 → 3 Jul 2008 |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science