Good article.
Not sure if anyone has made frequency measurements between the difference of
Richardson-Lucy or Max Entropy deconvolution formulas. It is believed that Max Entropy works best on lower frequencies. Have not had the time for pixel level of analysis of late. Either can perform wonders on an image if you've got good data.
Deconvolution can wreak havoc on faint wisps of nebulosity - some disappear, thus you lose valuable data. If you're trying to maximise nebulosity I recommend staying away from heavy deconvolution. Keep the iterations light if you must - a minimalistic approach. A better approach is to layer a deconvoluted image over the background (non deconvoluted image), then blend the two in PS. That way you get the best attributes of both images - Faint wisps from the non deconvoluted image and the detail and structure of the deconvoluted image which you use a layer mask to highlight details for. This works reasonably well.
A new image processing function has recently come to light. Developed by Ken Crawford I believe. It involves a few identical images that have been through different stages of deconvolution. These images are then combined in PS which displays an incredible detail (assuming good data). I had a URL to it somewhere. Will see if I can find it.
Again, well done. The more information to share around the better.