not that it's even close.

Also I read around on neural networks and soft computing, as so of the literature exists that at one point it seemed that was all there was.

finally I think its time to that so here goes:

Unless I'm mistaken what the authors are suggesting we would rather do, but cannot, is a practical impossibility:

to increase the signal at the expense of noise,

*****So that only left one possibly,*****

to increase the noise at the expense of the signal.

the rest are theories which obviously involved degrading the image (point spreads), adding noise, then optimizing error functions.

The functions are neural networked from the start with fuzzy set (and that's it) involved, incredibly late into the book.

The mathematics was pretty juicy. this is the primary redeeming quality.

The only flaw was that most of the books is on image restoration (why didn't it say so on the cover?) the last two chapter of the books were the hot topics.

Simply put this must have been the best of book.

-ok, I admit it was dated. but I hope you enjoyed the book-