The ‘Bussgang’ is one of the most known blind deconvolution algorithms. It requires the prior knowledge of the source statistics as well as the deconvolution noise characteristics. In this paper we present a first attempt for making the algorithm ‘more blind’ by replacing the original Bayesian estimator with a flexible parametric function whose parameters adapt through time. To assess the effec...

Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even wi...

The authors review the theoretical basis of determination of cerebral blood flow (CBF) using dynamic measurements of nondiffusible contrast agents, and demonstrate how parametric and nonparametric deconvolution techniques can be modified for the special requirements of CBF determination using dynamic MRI. Using Monte Carlo modeling, the use of simple, analytical residue models is shown to intro...

In the following paper we investigate two algorithms for blind signal deconvolution that has been proposed in the literature. We derive a clear interpretation of the information theoretic objective function in terms of signal processing and show that only one is appropriate to solve the deconvolution problem, while the other will only work if the unknown filter is constrained to be minimum phas...

This paper describes a technique for the blind deconvolution of extended objects such as Hubble Space Telescope ( H S T ) , scanning electron and 3D fluorescence microscope images. T h e blind deconvolution mechanism i s based o n the Richardson-Lucy algorithm and alternates between deconvolution of the image and point spread func t ion ( P S F ) . This f o r m of iterative blind deconvolution ...

Three-dimensional microscopy by computational deconvolution methods requires accurate knowledge of the point spread function (PSF) that characterizes the microscope. Experimental PSF’s can only be measured over small regions about focus because the small objects necessary for PSF measurement are dim. Theoretical computation of the PSF requires accurate knowledge of all the experimental setup pa...

Deconvolution refers to the problem of estimating the unknown input to an LTI system when the output signal and system response are known. In practice, the available output signal is also noisy. For some systems, the deconvolution problem is quite straight forward; however, for systems that are non-invertible or nearly non-invertible (e.g. narrow-band or with frequency response nulls), the prob...

OF THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science Optical Science and Engineering The University of New Mexico Albuquerque, New Mexico May, 2007

We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing.