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Killedar, V and Seelamantula, CS (2022) COMPRESSIVE PHASE RETRIEVAL BASED ON SPARSE LATENT GENERATIVE PRIORS. In: 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 23 - 27 May 2022, Virtual, Online at Singapore, pp. 1596-1600.

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Official URL: https://doi.org/10.1109/ICASSP43922.2022.9746985


We address the problem of compressive phase retrieval (CPR) based on generative prior. The problem is ill-posed and requires structural assumptions. CPR techniques impose sparsity prior on the signal to perform reconstruction from compressive phaseless measurements. Recent developments in data-driven signal models in the form of generative priors have been shown to outperform sparsity priors with significantly fewer measurements. However, it is possible to improve upon the performance of generative prior based methods by introducing structure in the latent-space. We propose to introduce structure on the signal by enforcing sparsity in the latent-space via proximal method while training the generator. The optimization is called as proximal meta-learning (PML). Enforcing sparsity in the latent space naturally leads to a union-of-submanifolds model in the solution space. The overall framework of imposing sparsity along with PML is called as sparsity-driven latent space sampling (SDLSS). We demonstrate the efficacy of the proposed framework over the state-of-the-art deep phase retrieval (DPR) technique on MNIST and CelebA datasets. We evaluate the performance as a function of the number of measurements and sparsity factor using standard objective measures. The results show that SDLSS performs better at higher compression ratio and has faster recovery compared with DPR. © 2022 IEEE

Item Type: Conference Paper
Publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 21 Jun 2022 10:23
Last Modified: 21 Jun 2022 10:23
URI: https://eprints.iisc.ac.in/id/eprint/73933

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