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 main algorithm is prepared as demo, so that it can be executed in a straightforward manner.
This demo reproduces figures and results from the paper:
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.
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.
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