THESIS DEFENSE: Seeing Beyond Limits with Physics-Informed Priors

Speaker: Yang Liu

Speaker Affiliation: MIT EECS & CSAIL

Host: Frédo Durand

Host Affiliation: MIT EECS & CSAIL

Date: Tuesday, July 15, 2025

Time: 2:00 PM to 3:00 PM

Location: 32-D463 (Star) or Zoom Link: https://mit.zoom.us/j/98534109114

Abstract:

Conventional imaging systems face inherent dimensionality and visibility limits, primarily because image sensors are typically two-dimensional, and light tends to diffuse on rough surfaces or scatter within complex media. In this talk, I will reframe imaging systems through the lens of optical encoding and neural decoding, presenting my key contributions aimed at transcending the traditional limits of dimensionality and visibility. The idea is modelling the forward physical process and iteratively optimizing it with deep denoisers as visual priors, where eventually the priors are physics-informed. First, I introduce Privacy Dual Imaging, which reveals the privacy risk that ambient light sensors embedded in most smart devices could capture images of the scene in front of the screen. This idea of seeing the invisible from subtle intensity fluctuations is inspired by George Orwell’s novel 1984, wherein Big Brother is watching you through a two-way telescreen, and it closely relates to incoherent lensless imaging and non-line-of-sight imaging. Second, I present Snapshot Compressive Imaging, which encodes multiple temporal, spectral, or angular frames into a single measurement captured by a standard two-dimensional sensor. By learning high-dimensional visual priors from image or video data, we can efficiently reconstruct the original higher-dimensional data cube at scale. Lastly, I show that large AI models, particularly diffusion models, can serve as generic visual priors for both cases and beyond. I aim to push the boundaries of imaging and sensing within relevant domains of AI for science and healthcare (with an example).

Committee Members: Frédo Durand (advisor, MIT), William T. Freeman (MIT & Google), Kaiming He (MIT & Google)

Relevant URL: https://mit.zoom.us/j/98534109114

For more information please contact: Roger White <whiter@mit.edu>

Add to Calendar 2025-07-15 14:00:00 2025-07-15 15:00:00 America/New_York THESIS DEFENSE: Seeing Beyond Limits with Physics-Informed Priors THESIS DEFENSE: Seeing Beyond Limits with Physics-Informed PriorsSpeaker: Yang LiuSpeaker Affiliation: MIT EECS & CSAILHost: Frédo DurandHost Affiliation: MIT EECS & CSAILDate: Tuesday, July 15, 2025Time: 2:00 PM to 3:00 PMLocation: 32-D463 (Star) or Zoom Link: https://mit.zoom.us/j/98534109114Abstract:Conventional imaging systems face inherent dimensionality and visibility limits, primarily because image sensors are typically two-dimensional, and light tends to diffuse on rough surfaces or scatter within complex media. In this talk, I will reframe imaging systems through the lens of optical encoding and neural decoding, presenting my key contributions aimed at transcending the traditional limits of dimensionality and visibility. The idea is modelling the forward physical process and iteratively optimizing it with deep denoisers as visual priors, where eventually the priors are physics-informed. First, I introduce Privacy Dual Imaging, which reveals the privacy risk that ambient light sensors embedded in most smart devices could capture images of the scene in front of the screen. This idea of seeing the invisible from subtle intensity fluctuations is inspired by George Orwell’s novel 1984, wherein Big Brother is watching you through a two-way telescreen, and it closely relates to incoherent lensless imaging and non-line-of-sight imaging. Second, I present Snapshot Compressive Imaging, which encodes multiple temporal, spectral, or angular frames into a single measurement captured by a standard two-dimensional sensor. By learning high-dimensional visual priors from image or video data, we can efficiently reconstruct the original higher-dimensional data cube at scale. Lastly, I show that large AI models, particularly diffusion models, can serve as generic visual priors for both cases and beyond. I aim to push the boundaries of imaging and sensing within relevant domains of AI for science and healthcare (with an example).Committee Members: Frédo Durand (advisor, MIT), William T. Freeman (MIT & Google), Kaiming He (MIT & Google)Relevant URL: https://mit.zoom.us/j/98534109114For more information please contact: Roger White <whiter@mit.edu>  TBD

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Thesis Defense

THESIS DEFENSE: Seeing Beyond Limits with Physics-Informed Priors

July 15 2025

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