Overview 

This tutorial focuses on emerging computational imaging applications, particularly addressing lesser-known inverse problems. Our objective is to shed light on crucial yet lesser-studied areas like snapshot compressive imaging and single-photon imaging, offering insights into their mathematical modeling, advancements, and the limitations of current solutions. In doing so, we also emphasize how AI/ML approaches are instrumental in addressing these challenges, enhancing traditional optimization-based methods to develop state-of-the-art algorithms in computational imaging.

Schedule

1) Introduction to Inverse Problems in Imaging (45 minutes):  Slides

Overview of Inverse Problems in Imaging

Classic Approach to Solving Inverse Problems:

• •   Utilizing simple convex regularizers to model the source structure

  •   Formulating the inverse problem as a convex optimization problem

Challenges and Limitations of Traditional Methods:

• • • Convex regularization methods and their limitations in capturing complex source structures

    •  Predominant focus on linear or generalized linear models

Deep Learning in Inverse Problems:

• •   Objectives:

    • Advancing beyond simplistic source models

    • Developing versatile algorithms for various inverse problems

  Potential Deep Learning-Based Approaches and Their Pros and Cons:

• End-to-end solutions

• Plug-and-play iterative methods

• Unrolled network architectures

2) Snapshot Compressive Imaging (SCI) (1 hour):  Slides

Defining the Problem: Recovering a 3D data cube from a single 2D projection

Applications of SCI

Mathematical Modeling of the SCI Inverse Problem

Deep Learning-Based Methods for SCI

3) Single-Photon Imaging (1 hour): Slides

Introduction to Ultrafast Imaging Technologies

Exploring Single-Photon Imaging and Its Mathematical Model

Non-Line-of-Sight Imaging:

• •   Problem Definition

  •   Classical Approaches to Non-Line-of-Sight Imaging

Deep Learning-Based Approaches to Non-Line-of-Sight Imaging

4) Compressive Coherent Imaging (45 minutes): Slides

Coherent Imaging: Classic Challenges in an Unsolved Context

Problem Definition: Imaging from Compressive Measurements in the Presence of Speckle Noise

Review of Mathematical Modeling and Recent Theoretical Developments

Deep Learning-Based Methods for Compressive Coherence Imaging

Audience 

• Target audience: Machine learning and applied math researchers and practitioners

•  Prerequisite information: 

  • Foundational understanding of basic linear algebra and convex optimization. 

  • Prior knowledge in deep learning and underdetermined linear inverse problems is advantageous, but not required.

• Registration information can be found here:

  • https://aaai.org/aaai-conference/registration/

Organizers