The ability to reverse is very dependent on the transformation being well known, in this case it is deterministic and known with certainty. Any algorithm to reverse motion blur will depend on the translation and rotation of the camera in physical space, and the best the algorithm could do will be limited by the uncertainty in estimating those values.

If you apply a fake motion blur like in photoshop or after effects then that could probably be reversed pretty well.

> and the best the algorithm could do will be limited by the uncertainty in estimating those values

That's relatively easy if you're assuming simple translation and rotation (simple camera movement), as opposed to a squiggle movement or something (e.g. from vibration or being knocked). Because you can simply detect how much sharper the image gets, and hone in on the right values.

I recall a paper from many years ago (early 2010s) describing methods to estimate the camera motion and remove motion blur from blurry image contents only. I think they used a quality metric on the resulting “unblurred” image as a loss function for learning the effective motion estimate. This was before deep learning took off; certainly today’s image models could do much better at assessing the quality of the unblurred image than a hand-crafted metric.

Record gyro motion at time of shutter?