Research On Characteristic Selecting Of Wavelet Thresholding And Denoise

The image is often corrupted by noise in its collection, acquisition or transmission, such as astronomy, medical, remote sensing imaging, and computer vision. The corrupted image is called noisy image. Because the noise is the main factor that influenced image quality and greatly affected to extract the information from it, it must be removed before it can be analyzed and utilized.Improving the signal-to-noise radio is an important task in the seismic data processing, therefore the study of de-noising methods is always hot in seismic exploring. With the development of digital signals processing technology, many excellent de-noising methods come forth. In the seismic data de-noising, there is an important research aspect that choosing the suitable de-noising method based on the feature of seismic data.Wavelet analysis is local analysis in the time domain and frequency domain, and which represents the signal property using combination of the time domain and frequency domain. It is a useful tool to analyze the unstationary signal that implements multiscale analysis to the signal by the translation and dilation of the mother wavelet. So it can effectively extract information from signal. At present, wavelet analysis is international acknowledged advanced technology in the domain of information and signal processing. Meanwhile it is the front question for discuss and study hotspot attached more and more importance to people in the domain such as signal filtering, image denoising, image coding, image edge detecting, image fusing etc.This dissertation studies focusing on signal filtering and image denoising. The main works of this dissertation are as follows:1. This dissertation introduces the history and the state of arts of image denoising, it also points out the advantages of wavelet other traditional methods and the importance of developing the study of wavelet image denoising.2. This dissertation elaborates some basic theories about the wavelet image denoising, especially on kinds of wavelet transforms, multi-resolution analysis, Mallat etc.3 For suppressing the artifacts of traditional wavelet thresholding denoising algorithm under the GWN(Gaussian White Noise)background,the dissertation generalizes the default threshold, penalty threshold, Birge-Massart threshold, SURE threshold, minimaxi threshold, heursure threshold and sqtwolong threshold, meanwhile, use Soft-thresholding and Hard-thresholding function to process the signal. The reconstructed signal is visually smoother and much better approaches the original signal. The one dimension signal and two dimension seismic section plane simulation tests validate the denoising performance.