336 research outputs found
BM3D Frames and Variational Image Deblurring
A family of the Block Matching 3-D (BM3D) algorithms for various imaging
problems has been recently proposed within the framework of nonlocal patch-wise
image modeling [1], [2]. In this paper we construct analysis and synthesis
frames, formalizing the BM3D image modeling and use these frames to develop
novel iterative deblurring algorithms. We consider two different formulations
of the deblurring problem: one given by minimization of the single objective
function and another based on the Nash equilibrium balance of two objective
functions. The latter results in an algorithm where the denoising and
deblurring operations are decoupled. The convergence of the developed
algorithms is proved. Simulation experiments show that the decoupled algorithm
derived from the Nash equilibrium formulation demonstrates the best numerical
and visual results and shows superiority with respect to the state of the art
in the field, confirming a valuable potential of BM3D-frames as an advanced
image modeling tool.Comment: Submitted to IEEE Transactions on Image Processing on May 18, 2011.
implementation of the proposed algorithm is available as part of the BM3D
package at http://www.cs.tut.fi/~foi/GCF-BM3
Spatially adaptive estimation via fitted local likelihood techniques
This paper offers a new technique for spatially adaptive estimation.
The local likelihood is exploited for nonparametric modelling of observations
and estimated signals. The approach is based on the assumption of a local
homogeneity of the signal: for every point there exists a neighborhood in
which the signal can be well approximated by a constant. The fitted local
likelihood statistics is used for selection of an adaptive size of this
neighborhood. The algorithm is developed for quite a general class of
observations subject to the exponential distribution. The estimated signal
can be uni- and multivariable. We demonstrate a good performance of the new
algorithm for Poissonian image denoising and compare of the new method versus
the intersection of confidence interval technique that also exploits
a selection of an adaptive neighborhood for estimation
Spatially adaptive estimation via fitted local likelihood techniques
This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modelling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics is used for selection of an adaptive size of this neighborhood. The algorithm is developed for quite a general class of observations subject to the exponential distribution. The estimated signal can be uni- and multivariable. We demonstrate a good performance of the new algorithm for Poissonian image denoising and compare of the new method versus the intersection of confidence interval technique that also exploits a selection of an adaptive neighborhood for estimation
Lensless hyperspectral imaging by Fourier transform spectroscopy for broadband visible light: phase retrieval technique
A novel phase retrieval algorithm for broadband hyperspectral phase imaging
from noisy intensity observations is proposed. It utilizes advantages of the
Fourier Transform spectroscopy in the self-referencing optical setup and
provides, additionally beyond spectral intensity distribution, reconstruction
of the investigated object's phase. The noise amplification Fellgett's
disadvantage is relaxed by the application of sparse wavefront noise filtering
embedded in the proposed algorithm. The algorithm reliability is proved by
simulation tests and results of physical experiments on transparent objects
which demonstrate precise phase imaging and object depth (profile)
reconstructions.Comment: 12 pages, 8 figure
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