Images restored using the RL iteration have good good photometric linearity and can be used for quantitative analysis, and typical RL restorations require a manageable amount of computer time. Despite its advantages, the RL method has some serious shortcomings.
Noise amplification is a problem, as for all maximum likelihood techniques.
The speckles are the result of fitting the noise in the data too closely. The only limit on the amount of noise amplification in the RL method is the requirement that the image not become negative. The usual practical approach to limiting noise amplification is simply to stop the iteration when the restored image appears to become too noisy. However, in most cases the number of iterations needed is different for different parts of the image. This paper will describe a new image restoration iteration that dramatically reduces noise amplification while retaining the good characteristics and efficiency of the RL method.