Today I gave a talk at STOR colloquium at the University of North Carolina (UNC). I spoke about the Master’s thesis of Lars Mescheder in which he developed probabilistic models for image denoising. Among other things, he proposed a Gaussian scale mixture model as an image prior and developed several methods to infer information from the respective posterior. One curios result was that the Perona-Malik diffusivity (and variants thereof) popped up in basically every algorithm he derived. It’s not only that the general idea to have an edge dependent diffusion coefficient in a PDE or, formally equivalent, a concave penalty for the magnitude of the gradient in a variational formulation, but it also turned out that the very diffusion coefficient appeared exactly in this form.
Besides the talk I got the chance to chat with many interesting people. I’d like to mention a chat about a background story on Perona-Malik which I find quite interesting:
Steven Pizer, Kenan Professor for computer science at UNC and active in a lot of fields for several decades, and in particular a driving for in the early days of digital image analysis, told that the very idea of the non-linear, gradient-magnitude dependent diffusion coefficient actually dates back to works of neuroscientists Stephen Grossberg and that he moderated a discussion between Stephan Grossberg, Pietro Perona and Jitendra Malik in which the agreed that the method should be attributed as “Grossberg-Perona-Malik” method. I could not find any more hints for this story anywhere else, but given the mathematical, psychological and biological background of Grossberg and a look at Grossbergs list of publications in the 1980’s and earlier make this story appear more than plausible.
It may also well be, that this story is just another instance of the effect, that good ideas usually pop up at more than one place…