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RF Imaging

From Wave Physics to Learned Reconstruction

From electromagnetic scattering theory to neural-network-based scene reconstruction. Covers inverse problems, compressed sensing, deep learning for imaging, ISAC, and neural scene representations.

32 chapters287 sections82+ hours of content
Start from Chapter 1

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Mathematical Foundations for Inverse ProblemsThe Physics: From Maxwell to the Forward ModelRF Sensing Systems and Signal ProcessingClassical Image ReconstructionBayesian and Message-Passing ReconstructionDeep Learning for Computational ImagingNeural Scene RepresentationsConnections to Medical Imaging and Computer VisionISAC: Integrated Sensing, Communication, and ImagingPractical Systems, Evaluation, and Open ProblemsFuture topics

All Chapters

Part 1: Mathematical Foundations for Inverse Problems

Chapter 1

Functional Analysis for Imaging

~200 minAdvanced
Chapter 2

Ill-Posed Problems and Regularization Theory

~200 minFoundational
Chapter 3

Bayesian Inverse Problems

~200 minIntermediate
Chapter 4

Computational Tools for Inverse Problems

~150 minIntermediate

Part 2: The Physics: From Maxwell to the Forward Model

Chapter 5

Electromagnetic Scattering Theory

~180 minIntermediate
Chapter 6

Caire's Unified Forward Model

~200 minAdvanced
Chapter 7

The Kronecker Structure and Sensing Operator Properties

~140 minAdvanced
Chapter 8

Scene Representation and Forward Model Variations

~130 minIntermediate

Part 3: RF Sensing Systems and Signal Processing

Chapter 9

Radar Signal Processing Fundamentals

~150 minIntermediate
Chapter 10

OFDM and OTFS Sensing

~150 minAdvanced
Chapter 11

MIMO Radar and Virtual Aperture

~150 minAdvanced
Chapter 12

Synthetic Aperture Imaging

~140 minIntermediate

Part 4: Classical Image Reconstruction

Chapter 13

Matched Filter and Backpropagation Imaging

~120 minIntermediate
Chapter 14

Sparse Reconstruction for RF Imaging

~140 minAdvanced
Chapter 15

Diffraction Tomography

~140 minIntermediate
Chapter 16

Phase Retrieval and Phaseless Imaging

~120 minIntermediate

Part 5: Bayesian and Message-Passing Reconstruction

Chapter 17

AMP and OAMP for RF Imaging

~180 minAdvanced
Chapter 18

Learned OAMP and Deep Unfolding for Imaging

~160 minAdvanced
Chapter 19

EM-Based Methods and Hyperparameter Estimation

~130 minAdvanced

Part 6: Deep Learning for Computational Imaging

Chapter 20

Post-Processing and Model-Based Networks

~150 minAdvanced
Chapter 21

Plug-and-Play and Regularization by Denoising

~160 minAdvanced
Chapter 22

Diffusion Models for Inverse Problems

~160 minAdvanced
Chapter 23

Self-Supervised and Unsupervised Methods

~150 minResearch

Part 7: Neural Scene Representations

Chapter 24

NeRF for RF

~160 minResearch
Chapter 25

Implicit Geometry from RF

~120 minAdvanced
Chapter 26

3D Gaussian Splatting for RF

~150 minAdvanced

Part 8: Connections to Medical Imaging and Computer Vision

Chapter 27

Medical Imaging: CT, MRI, and Ultrasound

~150 minAdvanced
Chapter 28

Multi-View Geometry and Differentiable Rendering

~160 minAdvanced

Part 9: ISAC: Integrated Sensing, Communication, and Imaging

Chapter 29

ISAC Waveform Design and Beamforming

~140 minResearch
Chapter 30

From ISAC to Joint Communication-Sensing-Imaging

~160 minResearch

Part 10: Practical Systems, Evaluation, and Open Problems

Chapter 31

Hardware, Datasets, Simulation, and Evaluation

~120 minAdvanced
Chapter 32

Open Problems and Future Directions

~150 minResearch