A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise

Image diffusion using saliency bilateral filter

Image diffusion can smooth away noise and small-scale structures while retaining important features, thereby enhancing the performances of many image processing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency measure which is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. The connection between filtering kernels and perceptual saliency makes it possible to remove small-scale structures and preserves significant boundaries adaptively. The effectiveness of the proposed approach is validated by experiments on various medical images including the color Chinese Visible Human data set and gray MRI brain images

Image Diffusion Using Saliency Bilateral Filter

Image diffusion can smooth away noise and small-scale structures while retaining important features, thus improving the performances for many image processing algorithms. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries. The effectiveness of the proposed approach is validated by experiments on various medical images

Image Diffusion Using Saliency Bilateral Filter

Image diffusion can smooth away noise and small-scale structures while retaining important features, thereby enhancing the performances of many image processing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency measure which is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. The connection between filtering kernels and perceptual saliency makes it possible to remove small-scale structures and preserves significant boundaries adaptively. The effectiveness of the proposed approach is validated by experiments on various medical images including the color Chinese Visible Human data set and gray MRI brain images. © Springer-Verlag Berlin Heidelberg 2006

Image Diffusion Using Saliency Bilateral Filter

Image diffusion can smooth away noise and small-scale structures while retaining important features, thus improving the performances for many image processing algorithms. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries. The effectiveness of the proposed approach is validated by experiments on various medical images. © 2008 IEEE