Image restoration by parallel simulated annealing using compound Gauss-Markov models
A parallel simulated annealing algorithm is presented for image restoration using a compound Gauss-Markov field model for the image. Results are provided for its implementation on a distributed array processor (DAP510) which is a single instruction multiple data (SIMD) machine with 1024(32*32) mesh-connected processor elements, and a clock rate of 10 MHz. The total time required for restoring a monochrome blurred and noisy image with continuous range of intensities is reduced to about 10 minutes as compared to 20 hours for its sequential implementation (VAX11/785). Both the maximum a posteriori (MAP) and minimum mean square error (MMSE) estimates of the original image are obtained. The parallel estimates are shown, as well as the sequential estimate and the classical Wiener filter estimate.<<ETX>>