A blind deconvolution algorithm for simultaneous image restoration and system characterisation

The restoration of a blurred image in a practical imaging system is critically dependent on the system point spread function. Measurement of the point spread function is often a difficult and time consuming process, and the measurement environment itself is somehow artificial. Also, it is frequently the case that an observed image and the point spread function are not measured simultaneously under the same conditions. An iterative blind deconvolution algorithm is presented here which is capable of restoring an image without the need for an exact estimate of the point spread function. The ideal image and the point spread function can be estimated simultaneously by imposing appropriate a priori constraints. Typical experimental results are presented and discussed.