From 1992. It’s a discussion of a paper by Donoho, Johnstone, Hoch, and Stern. As I summarize:
Under the “nearly black” model, the normal prior is terrible, the entropy prior is better and the exponential prior is slightly better still. (An even better prior distribution for the nearly black model would combine the threshold and regularization ideas by mixing a point mass at 0 with a proper distribution on [0, infinity].) Knowledge that an image is nearly black is strong prior information that is not included in the basic maximum entropy estimate.
Overall I liked the Donoho et al. paper but I was a bit disappointed in their response to me. To be fair, the paper had lots of comments and I guess the authors didn’t have much time to read each one, but still I didn’t think they got my main point, which was that the Bayesian approach was a pretty direct way to get most of the way to their findings. To put it another way, that paper had a lot to offer (and of course those authors followed it up with lots of other hugely influential work) but I think there was value right away in thinking about the different estimates in terms of prior distributions, rather than treating the Bayesian approach as a sort of sidebar.