Noise suppression and barrier crossing in Monte Carlo image-restoration method
Abolfazl M. Amini · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In this paper, an efficient approach for image restoration of noisy data is suggested. This approach combines the Monte Carlo image restoration technique and the Morrison noise removal methods. The mean squared error (MSE) criterion is used to test the performance of the Monte Carlo method with and without prior-application of the Morrison noise removal method. The methods for facilitating the Monte Carlo walk to the brightest regions of the image are discussed and a new approach is suggested. It is shown that the Monte Carlo technique is potentially very fast with good resolution. The Morrison noise removal method smoothes the data at the first iteration and proceeds to restore the data back to its original noisy form at later iterations. To achieve some noise suppression, one can stop the Morrison iterations before it converges to the original noisy form. The Monte Carlo method is then applied to the noise suppressed data.