HPM-Based Dynamic Wavelet Transform and Its Application in Image Denoising

Abstract EN

Wavelet-based multiscale interpolation operator is often employed to construct the adaptive numerical method for PDEs, in which the computational complexity of the wavelet transform is one of the main factors affecting the algorithm efficiency.

As the wavelet transform just acts as the detector of the characteristic points in the interpolation operator, the multiscale wavelet interpolation operator can be viewed as a nonlinear problem.

Based on this assumption, we construct an approximate dynamic interpolation operator with the homotopy perturbation method (HPM), which decreases the computational complexity of the wavelet transform appearing in the wavelet interpolation operator from O((1/3)42J−1) to O(4J), where J is the amount of the wavelet scales.

Then an adaptive algorithm solving the Perona-Malik model on image denoising is constructed with the HPM-based interpolation operator.

Last, the quasi-Shannon wavelet is employed to design the experiments on the medical image and some artificial images denoising.

The experiment results show that the simplified wavelet interpolation operator based on HPM possesses the adaptability and nonsensitivity to the time step, which is helpful to improve the algorithm efficiency.

This illustrates that the HPM-based wavelet interpolation operator is an effective tool to solve the problems in image processing.