Although lots of study has been conducted on blind deconvolution, I was frustrated to see that even state-of-art convolution algorithms don’t work very well.
Of course this has something to deal with the illposedness of the original problem (say, the Fourier Spectrum of PSF can have lots of zeros), I found the devil also resides in the formulation of the problems, e.g. noise model, prior to use, etc.

IMHO the algorithm that works best in practice is Richardson-Lucy algorithm developed in 1970s. I bet it is because this algorithm uses a Poisson distribution to model the shot noise in camera. Wavelet regularized algorithm is my second favorite because it also produces natural results, although because of the wavelet bases, they do produce noise.

I personally don’t like diffusion based approach since the output image would be piecewise linear. If that is the case, why not just do edge detection and then inpaint? I would rather do L2-regularized or Wiener deconvolution in that case.