Projects per year
Abstract
Many modern imaging applications can be modeled as compressed sensing linear inverse problems. When the measurement operator involved in the inverse problem is sufficiently random, denoising Scalable Message Passing (SMP) algorithms have a potential to demonstrate high efficiency in recovering compressed data. One of the key components enabling SMP to achieve fast convergence, stability and predictable dynamics is the Onsager correction that must be updated at each iteration of the algorithm. This correction involves the denoiser's divergence that is traditionally estimated via the Black-Box Monte Carlo (BB-MC) method. While the BB-MC method demonstrates satisfying accuracy of estimation, it requires heuristic tuning and executing the denoiser additional times at each iteration and might lead to a substantial increase in computational cost of the SMP algorithms. In this work we develop two Large System Limit models of the Onsager correction for denoisers operating within SMP algorithms and use these models to propose practical black-box methods for divergence estimation that require no additional executions of the denoiser and demonstrate similar correction compared to the BB-MC method.
Original language | English |
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Pages (from-to) | 7461-7477 |
Number of pages | 17 |
Journal | IEEE Transactions on Information Theory |
Volume | 69 |
Issue number | 11 |
Early online date | 3 Jul 2023 |
DOIs | |
Publication status | Published - 1 Nov 2023 |
Keywords / Materials (for Non-textual outputs)
- cs.IT
- math.IT
- message passing
- Divergence Estimation
- Denoiser
- Onsager Correction
- expectation propagation
- denoiser
- Message passing
- divergence estimation
- Onsager correction
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Dive into the research topics of 'Divergence Estimation in Message Passing algorithms'. Together they form a unique fingerprint.Projects
- 2 Finished
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Next Generation Compressive and Computational Sensing and Signal Processing
1/10/16 → 30/09/21
Project: Research
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C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing
1/09/16 → 31/08/22
Project: Research