Showing papers by "Alessandro Foi published in 2010"

From Local Kernel to Nonlocal Multiple-Model Image Denoising

Abstract: We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain filtering based on nonlocal block-matching. The considered methods are classified mainly according to two main features: local/nonlocal and pointwise/multipoint. Here nonlocal is an alternative to local, and multipoint is an alternative to pointwise. These alternatives, though obvious simplifications, allow to impose a fruitful and transparent classification of the basic ideas in the advanced techniques. Within this framework, we introduce a novel single- and multiple-model transform domain nonlocal approach. The Block Matching and 3-D Filtering (BM3D) algorithm, which is currently one of the best performing denoising algorithms, is treated as a special case of the latter approach.

Denoising of single-look SAR images based on variance stabilization and nonlocal filters

Abstract: Synthetic-aperture radar (SAR) imaging has become an efficient tool for obtaining and retrieving useful information about surfaces of Earth and other planets. However, the formed images suffer from speckle noise, especially if single-look observation mode is used. Then, filtering is often applied to improve image quality and provide better estimation of radar cross-section and other parameters of sensed scenes. Recently, a novel class of image filters has proved to be very successful in the removal of additive white Gaussian noise from natural images; these filters are based on nonlocal image modeling, i.e. they exploit the mutual self-similarity of image patches at different locations in the image. These filters have been shown in several benchmarks to significantly outperform all previous techniques. In this paper, we evaluate the performance of nonlocal filters applied to the denoising of single-look SAR images corrupted by speckle with a Rayleigh distribution, taking advantage of exact forward and inverse variance-stabilizing transformations. Numerical simulations demonstrate the success of this approach against several known despeckling methods.

Denoising of Multispectral Images via Nonlocal Groupwise Spectrum-PCA.

Abstract: We propose a new algorithm for multispectral image denoising. The algorithm is based on the state-of-the-art Block Matching 3-D lter. For each “reference” 3-D block of multispectral data (sub-array of pixels from spatial and spectral locations) we nd similar 3-D blocks using block matching and group them together to form a set of 4-D groups of pixels in spatial (2-D), spectral (1-D) and “temporally matched” (1-D) directions. Each of these groups is transformed using 4-D separable transforms formed by a xed 2-D transform in spatial coordinates, a xed 1D transform in “temporal” coordinate, and 1-D PCA transform in spectral coordinates. Denoising is performed by shrinking these 4-D spectral components, applying an inverse 4-D transform to obtain estimates for all 4-D blocks and aggregating all estimates together. The effectiveness of the proposed approach is demonstrated on the denoising of real images captured with multispectral camera.