In this paper, we propose a new dyadic wavelet-based conduction approach for selective image smoothing. In our approach, a nonlinear conductivity function is considered in the wavelet-based function decomposition and reconstruction process. Since the proposed approach does not require to solve a PDE, it is therefore more efficient and accurate than the conventional nonlinear diffusion/conduction-based methods. Experimental results using both 1-D synthetic data and a real image demonstrated that the proposed method can efficiently remove noises and preserve real data.
|Number of pages||9|
|State||Published - 1 Dec 1999|
|Event||Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA|
Duration: 23 Aug 1999 → 25 Aug 1999
|Conference||Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)|
|City||Madison, WI, USA|
|Period||23/08/99 → 25/08/99|