Thanks for the feedback. We are getting some interesting discussion in this rejuvenated thread:)
There is a lot of info to share, and people to convince ...
As you suggested, I did use RawTherapee to render the f/16 image.
The Gaussian radius was smaller than with the ACR rendering, 0.9922, and I used your tool to calculate a deconvolution PSF for 5x5 and 7x7.
Great, RT Amaze is always interesting to have in a comparison, because it is very good at resolving fine detail with few artifacts (and optional false color suppression).
Here is the image restoration in ImagesPlus with 20 iterations of RL using 7x7. There is quite a bit of artifact.
Using RL and a 5x5 kernel with 20 iterations, there again less artifact:
I see what you mean, and looking at the artifacts there may be something that can be done. No guarantee, but I suspect that deconvolving with a linear gamma can help quite a bit. In ImagesPlus one can convert an RGB image into R+G+B+L layers, deconvolve the L layer, and recombine the channels into an RGB image again. However, before and after deconvolution, one can switch the L layer to linear gamma and back (gamma 0.455 and gamma 2.20 will be close enough).
It can also help to temporarily up-sample the image before deconvolution. The drawback of that method is the increased time required for the deconvolution calculations, and it is possible that the re-sampling introduces artifacts. The benefit though is than one can visually judge the intermediate result (which is sort of sub-sampled) until deconvolution artifacts start to appear, and then downsample to the original size to make the artifacts visually less important.
Van Cittert with the 5x5 kernel and 20 iterations produces the best results.
In this case it does, but with more noise it may not be as beneficial. Also in this case, deconvolving the linear gamma luminance may work better.
Then there is another thing, and that will change the shape of the Gaussian PSF a bit. Creating the PSF kernel with my PSF generator defaults to a sensel arrangement with 100% fill factor (assuming gapless microlenses). By reducing that percentage a bit the Gaussian will become a bit more spiky, gradually more like a point sample and a pure Gaussian.
I realize its a bit of work, but that's also why we need better integration of deconvolution in our Raw converter tools. Until then, we can learn a lot about what can be achieved and how important it is for image quality.
Finally, you can also try the RL deconvolution in RawTherapee, I don't know if that is applied with Linear gamma but it should be come clear when you compare images. As soon as barely resolved detail becomes darker than expected, it's usually gamma related.