Deconvolution can wield its magic but the output can vary considerably based on the quality of data. A strong data set will handle deconvolution far better than a weak data set. If your data has issues such as hot pixels or other anomalies, deconvolution will only magnify these issues. CCDStack's PC algorithm is one of the best around. Though there are plenty of other options to explore (
http://www.princeton.edu/~rvdb/image...aximDLnew.html for example). I do however go by a general rule...if you're looking to maximise the quantity of nebulosity, don't use heavy deconvolution across the entire image. In most cases, you'll find deconvolution will suppress the faint wisps. I typically go with a PC of 30 iterations across the image to simply tighten up the stars. A second (and third if warranted) heavily deconvolved layer is then introduced to further enhance specific features using masks. The more control you have over the data, the better the result you'll be able to achieve.