Statistical-information-based performance criteria for Richardson-Lucy image deblurring.
Iterative image deconvolution algorithms generally lack objective criteria for deciding when to terminate iterations, often relying on ad hoc metrics for determining optimal performance. A statistical-information-based analysis of the popular Richardson-Lucy iterative deblurring algorithm is presented after clarification of the detailed nature of noise amplification and resolution recovery as the algorithm iterates. Monitoring the information content of the reconstructed image furnishes an alternative criterion for assessing and stopping such an iterative algorithm. It is straightforward to implement prior knowledge and other conditioning tools in this statistical approach.
Prasad
Sudhakar
S
Center for Advanced Studies and Department of Physics and Astronomy, University of New Mexico, Albuquerque 87131, USA. sprasad@unm.edu