PDF-Understanding and evaluating blind deconvolution algorithms

UsingcapitallettersfortheFouriertransformofasignalYKXN3ThegoalofblinddeconvolutionistoinferbothkandxgivenasingleinputyAdditionallykisnonnegativeanditssupportisoftensmallcomparedtotheimage. Freeman MIT CSAIL Weizmann Institute of Science Hebrew University Adobe Abstract Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown Recent algorithms have afforded dramatic progress yet many as unibech Paolo Favaro University of Bern Bern Switzerland paolofavaroiamunibech Abstract In this paper we study the problem of blind deconvolu tion Our analysis is based on the algorithm of Chan and Wong 2 which popularized the use of sparse gradient nyuedu Terence Tay Chatham Digital ttaychathamdigitalcom Rob Fergus Courant Institute New York University ferguscsnyuedu Abstract Blind image deconvolution is an illposed problem that requires regularization to solve However many common forms of imag Sethares University of WisconsinMadison Electrical and Computer Engineering Department vuralcaewiscedu setharesengrwiscedu ABSTRACT In linear image restoration the point spread function of the degrading system is assumed known even though this infor Deconvolution is an indispensable tool in image processing and computer vision It commonly employs fast Fourier trans form FFT to simplify computation This operator however needs to t ransform from and to the frequency domain and loses spatial infor Texas A&M University and University of Technology Sydney. http://stat.tamu.edu/~carroll. Bayesian Methods for Density and Regression Deconvolution. Co-Authors.  . Bani. . Mallick. Abhra Sarkar . Refocusing and Defocusing. Wei Zhang, . Nember. , IEEE, and . Wai-Kuen. Cham, Senior Member, IEEE. Outline. Introdution. Background And Problem . Fomulation. Edge-Based Focus-Map Estimation. Image Refocusing By Blind . : We report observations of the asteroid 4 . Vesta. in the L’ (3.8 . μ. m) and M’ (4.7 . μ. m) wavelength bands. We observed on UT dates April 30 and May 1, 2007, using the Clio infrared camera on the MMT telescope with the adaptive secondary AO system in natural guide-star mode. Our observations are the first to resolve . Omid Alipourfard. , Masoud Moshref, Minlan Yu. {. alipourf. , . moshrefj. , . minlanyu. }@. usc.edu. Software Switches are Popular. Data centers. : . “Use the cloud to manage the cloud”. Load balancer and Firewall on VMs. and medical radiography. Adrian Leslie . Jannetta. Ph. D. Dissertation in 2005. Measure of Image Quality. MTF(Modulation Transfer Function) and Spatial Resolution. Point Spread Function. Signal to Noise Ratio. Amy C. Nau, O.D., F.A.A.O. University of Pittsburgh. UPMC Eye Center. McGowan Institute for Regenerative Medicine. Fox Center for Vision Restoration. Technology provides endless possibilities. for improving the lives of the visually impaired. Amy C. Nau, O.D., F.A.A.O. University of Pittsburgh. UPMC Eye Center. McGowan Institute for Regenerative Medicine. Fox Center for Vision Restoration. Technology provides endless possibilities. for improving the lives of the visually impaired. Confocal. . Microscopy . Volumes: Empirical determination of the point spread function . Eyal. . Bar-. Kochba. ENGN2500: Medical Imaging. Professor Kimia. What is Laser Scanning . Confocal. Microscopy (LSCM)?. CONCLUSIONS. METHODS. ACKNOWLEDGEMENTS. We now discuss our performance analysis. Our overall evaluation approach seeks to prove three hypotheses: (1) that . superpages. no longer affect optical drive throughput; (2) that mean response time is a bad way to measure effective power; and finally (3) that Byzantine fault tolerance no longer affect performance. We are grateful for distributed randomized algorithms; without them, we could not optimize for complexity simultaneously with complexity. We are grateful for noisy hierarchical databases; without them, we could not optimize for security simultaneously with performance. Our evaluation holds .