DEBLURRING of IMAGES by CELLULAR NEURAL NETWORKS with applications to
John P. MILLERt, Kenneth R. Crounset · 1994
In this paper it is shown how the Cellular Neural Network (CIVN) can be used to perform image and volume deblum'ng, with particular emphasis on applications to microscopy. We discuss the basic linear theory of the CNN including issues of stability and tem- plate size. It is observed that a CNN with a small template can be used to implement an Infinite Impulse Response filter. It is then shown how geneml deblurring problems can be addressed with a CNN when the blurring operator is known. The proposed application is to solve the basic 3-D confocal image reconstruction task of microscopy in real-time. It will be shown that under a reasonable sampling assumption about the form of the blurring operator, confocal behavior in microscope images can be obtained with only 3-5 acquired image planes. In addition, the stored program capability of the CNN Universal Machine would pro- vide integration of several image processing and detection task in the same architecture.