Morlet Wavelet and Its Application in Multi-Period Analysis of Climate Data(Hua Yi , Mar.25)

INPAC-Seminar 144

Title: Morlet Wavelet and Its Application in Multi-Period Analysis of Climate Data

Speaker:  Hua Yi , School of Mathematics and physics, Jinggangshan University

Location:  520 Pao Yue-Kong Library

Time: 10:20-11:20, Friday, March 25, 2014

Abstract:

The talk consists of two parts. In the first part, we analyze the theory of modified Morlet wavelet, and demonstrate the superiority of the modified Morlet wavelet through three aspects. We also discuss how to detect the period of the climate data, and obtain some meaningful results. Additionally, we propose an algorithm for the continuous wavelet transform, and the proposed method manifests a number of merits when analyzing the real-life signal, the climate data, over other methods.

In the second part, I give some statements about the future work. Following the oscillating theory of Y. Meyer, many image decomposition models have been proposed to split an image into two parts: structures and textures. But these models are not effective in the case of noisy image, because both textures and noise are oscillating patterns. We can characterize dyadic BMO norm and local variance by tight wavelet frame coefficients. Then we can propose a method to distinguish between texture and noise in wavelet domain. Furthermore, we can characterize Hardy norm by tight wavelet frame. Then we can give the algorithm for TV-H1 model. The research may make for the exquisite description of image and the fast computation.