Frequency domain blind deconvolution in multiframe imaging using anisotropic
spatially-adaptive denoising
for MATLAB version 6.5 or later
The MATLAB routine Multiframe Blind Deconvolution implements reconstruction of an
image
from multiple blurred and noisy observations. When knowledge about
image formation is incomplete, image processing based on multiple
observations of one scene aims to enhance comprehensive restoration quality.
The often problem of spatial misalignment of the registered
images is taken into account.
Classical fields of application are the astronomy, remote sensing, medical
imaging, etc. Multisensor data of different spatial, temporal, and spectral
resolutions are exploited for image sharpening, improvement of registration
accuracy, feature enhancement, and improved classification. Other examples can
be seen in digital microscopy, where the same specimen may be recorded at
several different focus settings; or in multispectral radar imaging through a
scattering medium which has different transfer functions at different
frequencies.
The blind deconvolution proposed is a recursive gradient-projection
algorithm. The essential part of this algorithm is a signal-adaptive denoising technique. The spatially-adaptive LPA-ICI denoising works as a
data adaptive
regularizator (for signal and for PSFs). One of the benefits of this approach
concerns the ability to work with large images and with the large support of
PSF. The algorithm demonstrates good convergence properties and image
restoration quality.
The provided demo is open-source, and may be modified and tuned to be exploited with other data.
This DemoBox is available free-of-charge for educational and non-profit scientific research,
enabling others researchers to understand and reproduce our work. Any unauthorized use of the LASIP
routines for industrial or profit-oriented activities is expressively prohibited.
The main routine provided in this DemoBox is the following:
demo_MC_BlindDeconvolution.m
Deblurring of an image from
multiple blurred and noisy observations.
The blurring operators are assumed to be unknown. The model of noise
considered is an additive white Gaussian noise.
The recursive gradient-projection reconstruction incorporates the
signal-adaptive LPA-ICI denoising.
Any unauthorized use of the LASIP routines for industrial or profit-oriented activities is expressively prohibited.
By downloading any of the LASIP files, you implicitly agree to all the terms of the LASIP limited license.
Katkovnik V., D. Paliy, K. Egiazarian, and J. Astola, “Frequency domain blind deconvolution in multiframe imaging using anisotropic spatially-adaptive denoising”
, Proc. 14th European Signal Process. Conf., EUSIPCO 2006, Florence, September 2006.
