Research Initation:  Adaptive Image Restoration and Recovery
{'Code': '05010800', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'John Cozzens', 'PO_EMAI': None, 'PO_PHON': None}
The objectives of this project are to develop new algorithms for                regularized space-variant deconvolution based on adaptive Kalman                or ARMA (autoregressive moving average) filtering with                          applications in image restoration; to restore blurred images to                 extract information that would otherwise be unreadable; to                      identify blur transfer function from the degraded image itself                  and develop theoretical measures for blur identifiability; to                   suppress ringing artifacts that are due to space-invariant                      deconvolution.                                                                                                                                                  Image restoration is an ill-posed problem.  Small perturbations                 of the observed image due to noise may yield large variations in                the solution.  Regularization of ill-posed problems can be                      achieved by incorporating statistical or deterministic a priori                 information into the solution.  Shift-invariant regularization                  causes ringing artifacts; however, adaptive deconvolution                       suppresses ringing artifacts.  Adaptive deconvolution is also                   able to restore spatially variant degradations.  Image                          degradations usually vary by spatial coordinates and the source                 of these degradations is generally unknown.  Hence, it is of                    interest to develop adaptive restoration techniques that can                    identify the signal and degradation model parameters on-line                    based on a local observation window.                                                                                                                            ARMA filtering, adaptive Kalman filtering with on-line parameter                identification for regularized restoration of space-variant                     blurs, and adaptive regularization in a deterministic framework                 using the theory of convex projections (POCUS) are being                        investigated.  The blur identification problem is one of                        identifying a symmetric, noncausal, nonminimum-phase, finite-                   extent impulse response for a linear system.  Practical                         statistical methods to solve the blur identification problem                    based on spatial-domain ARMA model identification are being                     explored.
{'FirstName': 'A. Murat', 'LastName': 'Tekalp', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'A. Murat Tekalp', 'EmailAddress': 'tekalp@ece.rochester.edu', 'NSF_ID': '000200441', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Rochester', 'CityName': 'ROCHESTER', 'ZipCode': '146113847', 'PhoneNumber': '5852754031', 'StreetAddress': '910 GENESEE ST', 'StreetAddress2': 'STE 200', 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '25', 'CONGRESS_DISTRICT_ORG': 'NY25', 'ORG_UEI_NUM': 'F27KDXZMF9Y8', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ROCHESTER', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '4720', 'Text': 'SIGNAL PROCESSING SYS PROGRAM'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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