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Karen Egiazarian

Researcher at Tampere University of Technology

Publications -  603
Citations -  26910

Karen Egiazarian is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Image processing & Filter (signal processing). The author has an hindex of 53, co-authored 585 publications receiving 22477 citations. Previous affiliations of Karen Egiazarian include Nokia & Roma Tre University.

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.
Proceedings ArticleDOI

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte, +76 more
TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Journal ArticleDOI

Image database TID2013

TL;DR: This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics, and methodology for determining drawbacks of existing visual quality metrics is described.
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.