Wavelet And Partial Differential Equation Based Image Denoising

The image denoising is the earliest and most subject in the process image . Now there are two main methods of image denoising. One is wavelet analysis ,the other is partial differential equation(PDE).Analysis the advantages and disadvantages of the wavelet,based on invariance of translation and selectivity of multi direction to design the 6 ellipses windows to reducing noise. It can maximally denoising when keep the detail of image .The principle of PDE is diffusion the noise image .Because overlooking the feature of the image ,the diffusion is the same diffusion,destroy the edge and detail of image.Anisotropic diffusion is the key to solve the problemsA new image denoising method based on Dual-Tree complex wavelet combining partial differential equation in image denoising is presented. Image denoising with Wiener filter is not kept the edges of the image well enough because lack of directional ability in common tensor wavelet transform. Authors use the complex wavelet which has stronger directional ability and locally 6 directional Wiener filter to get a "clearer image", then use "clearer image" guidance the diffusion function of partial differential equation to reduce noise in the image .Results of experiment show: the recovered images have the higher PSNR and better visual quality than DTCW and PDE .