Date: Wednesday, October 16
Time: 3-4 p.m.
Location: BA5187 and online via Zoom. Enlarge image to scan QR code for Zoom link or visit: https://utoronto.zoom.us/meeting/register/tZUrduGrrT8sEtPIpr0eS8akSLExj8TKZ-Ss#/registration
There is no registration for this event. However, seating is limited, so arriving early is recommended.
Talk title: “Trustworthy and adaptive extreme low light imaging”
Abstract:
Imaging in low light settings is challenging due to low photon counts. In photography, imaging under low light, high gain settings often results in highly structured, non-Gaussian noise that’s hard to characterize or denoise. In scanning microscopy, the push to image faster, deeper, with less damage, and for longer durations, can result in noisy measurements and less signal acquired. In this talk, we'll address three problems in denoising that are important for real applications: 1) What can you do when your noise is sensor-specific and non-Gaussian? 2) How can you trust the output of a denoiser enough for critical scientific and medical applications? and 3) If you can sample a noisy scene multiple times, which parts should you resample? For the first problem, I'll introduce a sensor-specific, data-driven, physics-inspired noise model for simulating camera noise at the lowest light and highest gain settings. I'll then use this noise model as a building block for demonstrating photorealistic videography by the light of only the stars (submillilux levels of illumination). Next, I'll introduce an uncertainty quantification technique based on conformal prediction to simultaneously denoise and predict the pixel-wise uncertainty in microscopy images. Then, I'll use uncertainty-in-the-loop to drive adaptive acquisition for scanning microscopy, reducing the total scan time and light dose to the sample, while minimizing uncertainty.
Bio:
Kristina Monakhova is an Assistant Professor in the Department of Computer Science at Cornell University, where she leads the Computational Imaging Lab at Cornell. She received her PhD from UC Berkeley in Electrical Engineering and Computer Sciences and was a postdoctoral fellow at MIT, supported by the MIT Postdoctoral Fellowship for Engineering Excellence. Her research group focuses on co-designing optics and algorithms to create better, smaller, and more capable cameras and microscopes.