Research On Image Denoising Based On Multiscale Geometric Transform

Due to the imperfection of image acquisition systems and transmission channels, images are often corrupted by noise. This degradation to a significant reduction of image quality and then makes more difficult to perform high-level vision tasks such as image segmentation and image compression. So how to denoise the image to improve the image quality becomes a very important task in image processing.For the past few years, the basic idea of Multiscale Geometric Analysis(MGA) has developed a series of new theories independently in many areas such as pattern recognition and statistical analysis. This paper focuses on the local directional denosing methods based on the multiscale space. The main work of this paper includes:1. We describe a method for removing noise from digital images, based on bilateral filter and Gaussian scale mixtures (GSM) in shiftable complex directional pyramid (PDTDFB) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using a PDTDFB transform. Secondly, the bilateral filter, which is a nonlinear filter that does spatial averaging without smoothing edges, is applied on the approximation subband. Finally, the distribution of detail subbands of PDTDFB coefficients is modeled with GSM, and the statistical model is then used to obtain the denoised detail coefficients from the noisy image decomposition by Bayes least squares estimator. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.2. The Total Variation has been introduced in Computer Vision first by Rudin, Osher and Fatemi, as a regularizing criterion for solving inverse problems. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. We proposed an algorithm for minimizing the Total Variation, which we claim to be quite fast. It has been proved that the proposed method can preserve edges very well while removing noise.