Anti-blur software usually works by deconvolution. A blurry photo can be thought of as the original image plus a blur pattern and some noise. The software tries to estimate that blur pattern, then mathematically reverse it to recover detail.

A common method is based on Wiener deconvolution, which attempts to undo blur while limiting the amplification of noise. In simple terms, the program must estimate two things: what part of the image is true detail, and what part is unwanted blur/noise. That estimate is never perfect, which is why results vary.

This works best when the blur is relatively simple and predictable, such as mild motion blur or defocus. It works less well when the blur is severe, the image is very noisy, or important detail has been completely lost. In those cases, the software can only make an educated guess, sometimes creating artifacts or an overprocessed look.

So anti-blur tools are not really “restoring” lost information perfectly—they are using mathematical models to infer what the sharp image probably looked like.