Turning Motion Blur into a Design Advantage

Welcome.

Today we are excited to feature a pioneering voice in imaging system design, Dr. Michael John Phanis from Phanis Photonics.

He's redefining how we think about motion blur, not as a flaw to be eradicated, but as a powerful design tool that can unlock new possibilities in speed, precision, and computational imaging.

If you haven't heard of designing the blur budget before, don't worry.

We are going to unpack what it means and why embracing a little imperfection could lead to bolder, more efficient imaging architectures.

So whether you're an engineer, research integrator, or just fascinated by how AI is transforming machine vision, this episode promises some fresh perspectives and practical insights.

Dr. Fantas, Michael, thank you so much for joining us today.

Thanks so much for having me.

Thank you.

I really appreciate being here.

You bet.

So would you mind sharing just a little bit about your journey and how you became interested in continuous motion imaging and this unique approach to managing motion blur?

Absolutely.

So this all began during my doctoral program.

We were flirting a lot with different image-to-image ideas for biomedical purposes primarily.

This is around 2020, and it was commonplace to use devices like detection, classification, segmentation on histological data.

But converting one image to another still seemed to offer a lot of exciting results.

So that's something that we were pursuing actively.

We eventually turned our attention to motion blur by considering the degrees of freedom of the average digital microscope in our lab.

If you actually count the number of ways a modern microscope can be fiddled with physically, it comes up to over a dozen.

And so each knob or switch can be thought of as a potential opportunity for AI enhancement.

This particular concept, the motion blurring, is inherently an operational scheme where the method of acquisition revolves around an AI premise.

This is very different from, say, inferring one kind of a stain or image type or modality from another.

Here, you're seeking to improve the way an image is taken.

Other translations may involve the way a specimen is processed or the way the specimen is illuminated.