ML-blind deconvolution algorithm: recent developments

Santosh Bhattacharyya; Donald H. Szarowski; James N. Turner; Nathan J. O'Connor; Timothy J. Holmes

doi:10.1117/12.237475

10 April 1996 ML-blind deconvolution algorithm: recent developments

Santosh Bhattacharyya, Donald H. Szarowski, James N. Turner, Nathan J. O'Connor, Timothy J. Holmes

Proceedings Volume 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III; (1996) https://doi.org/10.1117/12.237475
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

The Maximum Likelihood based blind deconvolution (ML-blind) algorithm is used to deblur 3D microscope images. This approach was first introduced to the microscope community by us circa 1992. The basic advantage of a blind algorithm is that it simplifies the user interface protocols and reconstructs both the object and the Point Spread Function. In this paper we will discuss the recent improvements to the algorithm that robustize the performance and accelerate the speed of convergence. For instance, powerful and physically justified constraints are enforced on the reconstructed PSF at every iteration for robustization. A line search technique is added to the object reconstruction to accelerate the convergence of the object estimate. A simple modification to the algorithm enables adaptation for the transmitted light brightfield modality. Finally, we incorporate montaging in order to process large data fields.

Citation Download Citation

Santosh Bhattacharyya, Donald H. Szarowski, James N. Turner, Nathan J. O'Connor, and Timothy J. Holmes "ML-blind deconvolution algorithm: recent developments", Proc. SPIE 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III, (10 April 1996); https://doi.org/10.1117/12.237475

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