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on
1
Intro
2
Example: diffraction imaging with masks
3
Masked Fourier phase retrieval
4
Frames: definition
5
Phase retrieval with frames
6
Phase retrieval with shifting window: application example
7
Gabor frames and phase retrieval applications
8
Injectivity of phase retrieval
9
Stability of phase retrieval
10
Stability using frame order statistics
11
Challenges of Gabor phase retrieval
12
Phase retrieval algorithm for structured frames
13
Idea of the polarization approach
14
Polarization identity
15
Polarization approach: phase propagation algorithm
16
How large should set E be?
17
Reconstruction in the case of noisy measurements
18
Adaptive measurement design
19
Prior information
20
Sparse phase retrieval
21
Generative priors
Description:
Explore a comprehensive lecture on STFT Phase retrieval, focusing on robustness and generative priors. Delve into the non-convex inverse problem of signal reconstruction from intensity measurements, with applications in diffraction imaging and audio processing. Examine the use of Gabor frames in phase retrieval, discussing stable and efficient reconstruction methods. Investigate how generative models can regularize the phase retrieval problem, introduce prior information about signal classes, and reduce required measurements. Learn about injectivity, stability, and challenges in Gabor phase retrieval, as well as algorithms for structured frames. Discover the polarization approach, adaptive measurement design, and the incorporation of prior information in sparse phase retrieval and generative priors.

STFT Phase Retrieval - Robustness and Generative Priors - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)
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