Usage
It is important to understand two things about deconvolution as a basic, fundamental process;
- Deconvolution is "an ill-posed problem", due to the presence of noise in every dataset. This means that there is no one perfect solution, but rather a range of approximations to the "perfect" solution.
- Deconvolution should not be confused or equated with sharpening; deconvolution should be seen as a means to restore a compromised (distorted by atmospheric turbulence and/or diffraction by the optics) dataset. It is not meant as an acuity enhancing process or some sort of beautification filter. You should (will) always be able to corroborate the detail it restores, using the work from your peers, observatories and space agencies.
In addition to the above, deconvolution with a spatially variant Point Spread Function, adds to the complexity of basic deconvolution by requiring a model that accurately describes how the Point Spread Function changes across the image, rather than assuming a one-distortion-fits-all.
Understanding these important points will make clear why some of the various parameters exist in this module, and what is being achieved by the module.
