Abstract:

     In this this, I will introduce a novel iterated two-
band filtering method to solve the selective image 
smoothing problem. We prove that a discrete computation 
step in an iterated nonlinear diffusion-based filtering 
algorithm is equivalent to a sequence of operations, 
including decomposition, regularization, and then 
reconstruction, in the proposed two-band filtering scheme. 
To correctly separate the high frequency components from 
the low frequency ones in the decomposition process, we 
adopt a dyadic wavelet-based approximation scheme. In the 
regularization process, we use a diffusivity function as a 
guide to retain useful data and suppress noises. Finally, 
the signal of the next stage, which is a ˇ§smootherˇ¨ 
version of the signal at the previous stage, can be 
computed by reconstructing the decomposed low frequency 
component and the regularized high frequency component. 
Based on the proposed scheme, the smoothing operation can 
be applied to the correct targets. Experimental results 
show that our new approach is really efficient in noise 
removing.