Research On Image Denoising Based On Ridgelet Tranform

Many real-images are always corrupted by noise. In order to further image analysis and communication, the noise needs to be reduced in image pre-processing. Traditional methods can filter noise, but at the same time they make the image details blurred. Recently, with the development of wavelet theory, wavelet analysis has been applied in many fields. Meanwhile, wavelet is applied in image denoising successfully. And many new image denoising algorithms based on wavelet have been proposed.Wavelet transfonn has the characteristic of "mathematics microscope", thus it cannot only wipe off noise but also retain the image detail. Ridgelet transform is a new kind of multiscale analysis technique after wavelet transfonn. For image processing, ridgelet transform is more effective than the wavelet transform in representing linear and super-plane singularities. Among the wavelet denoising methods, Donoho's wavelet shrinkage appears early and is recognized by many researchers, but this method tends to kill too many wavelet coefficients that might contain useful image information, and the reconstruction error is a little bigger. Therefore, aiming at the threshold selection and how to deal with the threshold, people make much research.The content of this dissertation is Ridgelet transform and its application in image processing. The content is as followed:First, the common and wavelet image denoising methods are discussed, and the characteristics of these methods are compared;Then we study the traditional wavelet shrinkage threshold and improve Lakhwinder Kaur's NormalShrink by aiming at the defect of Donoho's VisuShrink. Third, based on the traditional threshold method, we propose the soft-hard threshold tradeoff function. Fourth, we analyze the Ridgelet transfonn and the image denoising algorithm based on it. Finally, we use soft-hard threshold tradeoff function to the finite ridgelet transform for image denoising applying the new adaptive shrinkage shreshold, and compare the traditional method on wavelet transform and Ridgelet transform for image denoising. For images feature by straight lines, the experimental results show our method is better than the one we apply the traditional method on wavelet transform. The PSNR and vision perceive are better.