Title :
Blind deconvolution: errors, errors everywhere
Abstract :
This paper focuses on a class of methods that accounts for uncertainty in the model as well as the data. Our example concerns spectroscopy - the attempt to reconstruct a true spectrum from an observed one. The problem we´re considering is sometimes called blind deconvolution, because we´re trying to unravel not only the spectrum, but the function that caused the blurring. These problems also arise in image deblurring.
Keywords :
blind source separation; deconvolution; image restoration; signal reconstruction; spectral analysis; spectroscopy; blind deconvolution; data uncertainty; error correction; image deblurring; model uncertainty; spectroscopy; spectrum reconstruction; Counting circuits; Deconvolution; Equations; Error correction; Home computing; Least squares methods; Matrix decomposition; Singular value decomposition; Spectroscopy; Uncertainty; error correction; modeling; uncertainty;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2005.10
