Edge and contrast preserving in total variation image denoising

Edge structure preserving image denoising

Image denoising is important in image analysis. It is often used for pre-processing images so that subsequent image analysis is more reliable. Besides noise removal, one important requirement for image denoising procedures is that they should preserve true image structures, such as edges. This paper proposes a novel denoising procedure which can preserve edges and major edge features (e.g., ang...

Edge Structure Preserving 3-D Image Denoising

In various applications, including magnetic resonance imaging (MRI) and functional MRI (fMRI), 3D images get increasingly popular. To improve reliability of subsequent image analyses, 3-D image denoising is often a necessary pre-processing step, which is the focus of the current paper. In the literature, most existing image denoising procedures are for 2-D images. Their direct extensions to 3-D...

Edge-preserving wavelet thresholding for image denoising

In this paper we consider a general setting for wavelet based image denoising methods. In fact, in both deterministic regularization methods and stochastic maximum a posteriori estimations, the denoised image f̂ is obtained by minimizing a functional, which is the sum of a data fidelity term and a regularization term that enforces a roughness penalty on the solution. The latter is usually define...

Total variation based image denoising and restoration

This paper is devoted to the total variation (TV) based approach to image denoising and restoration. The restored image minimizes total variation in the class of images which satisfy the contraints given by the image acquisition model. We compute some explicit solutions of the denoising model which explain some of the features observed in numerical experiments. We also comment on some alternati...

Image Segmentation and Total Variation Denoising