Deconvolution vs. AI 'Deblurring' for microscopy - Image Analysis - Image.sc Forum

Does anyone here have experience using or comparing general deconvolution methods with ‘AI’-based ‘de-blurring’ methods for microscopy data? I have seen some papers in the literature touching on this. I use the term ‘general deconvolution’ to mean mathematical methods that aim to recover the un-convolved image from a knowledge of the input image +/- PSF(s) only (this includes what basic priors can logically be known from them like non-negativity and any theoretical maximum intensity level in the solution). Personally, I don’t consider ‘AI’ (deep learning) methods that perform de-blurring to be ‘deconvolution’ because of their imputational nature attempting to fit extraneous data to a solution regardless of how plausible and wonderful the results may look. Am I alone in this view? Unfortunately (from my perspective) there are increasing numbers of peer-reviewed papers being published that permit the ‘D’-word to be used in conjunction with AI imputation methods. Do any of you have person

saved by

• Esther Lin