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Alessandro Foi
Researcher at Tampere University of Technology
Publications - 106
Citations - 17037
Alessandro Foi is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Noise reduction & Noise. The author has an hindex of 35, co-authored 99 publications receiving 13850 citations. Previous affiliations of Alessandro Foi include Nokia & University of Pennsylvania.
Papers
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Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Journal ArticleDOI
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
TL;DR: A signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data.
Journal ArticleDOI
Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction
TL;DR: Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.
Journal ArticleDOI
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
TL;DR: A novel approach to image filtering based on the shape-adaptive discrete cosine transform is presented, in particular, image denoising and image deblocking and deringing from block-DCT compression and a special structural constraint in luminance-chrominance space is proposed to enable an accurate filtering of color images.
Proceedings ArticleDOI
Image denoising with block-matching and 3D filtering
TL;DR: This work presents a novel approach to still image denoising based on effective filtering in 3D transform domain by combining sliding-window transform processing with block-matching, and shows that the proposed method delivers state-of-art Denoising performance, both in terms of objective criteria and visual quality.