Open AccessPhase retrieval for block sparsity based on adaptive coupled variational Bayesian learning
Author(s) -
Zhang Di,
Sun Yimao,
Bai Siqi,
Wan Qun
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12157
Subject(s) - block (permutation group theory) , robustness (evolution) , computer science , bayesian probability , algorithm , prior probability , computational complexity theory , computation , artificial intelligence , pattern recognition (psychology) , mathematics , biochemistry , chemistry , geometry , gene
Abstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.