Signal Based On Wavelet Theory, Image Processing Applications

The wavelet transformation is a new technique which was developed in these years, Since the past decade, wavelet transformation have been used in various signal and image processing tasks, such as for singularity edge detection and image denoising. Because of the good localization performances of time and frequency field, wavelet transformation gets a common use in signal and image denoising.With the inevitable presence of noise in these real-world applications, empirical wavelet coefficients with small magnitudes are likely to contain more noise than information. A simple idea is then to replace the small wavelet coefficients by zero, in the hope of recovering a less noisy signal after performing the inverse wavelet transform. This paper, the basic ideas applied wavelet theory to image denoising is proposed by studying principle of wavelet transformation and algorithm structure of dispersed wavelet transformation, analyzes the different characteristics of noise and imagine signal under wavelet transform. Several existing denoising techniques of imagine signal based wavelet transformation is investigated, these approaches take advantage of wavelet transform property and are theoretically and experimentally compared. Some techniques are performing in computer simulation, at last put forwards new viewpoints on signal and imagine denoising.