As a toy example if you have pixels a,b,c and you blur to (a+b)/2,(b+c)/2, you can recover back c - a. You might be able to have a good estimate of the boundary

As a toy example if you have pixels a,b,c and you blur to (a+b)/2,(b+c)/2, you can recover back c - a. You might be able to have a good estimate of the boundary condition ("a"), e.g. you can just use (a+b)/2 as an approximation for "a", so the recovered result might look fairly close. You'll basically want to look up the area of deconvolution. You can interpret it in linear algebra terms as trying to invert an ill-conditioned matrix, or in signal processing terms as trying to multiply by the inverse of the PSF. In real-world cases the main challenge is doing so without blowing up any error that comes from quantization noise (or other types of noise). See https://bartwronski.com/2022/05/26/removing-blur-from-images-deconvolution-and-using-optimized-simple-filters/ https://bartwronski.com/2022/05/26/removing-blur-from-images... and https://yuzhikov.com/articles/BlurredImagesRestoration1.htm https://yuzhikov.com/articles/BlurredImagesRestoration1.htm