Fast Robust Sparse Bayesian Learning Image Reconstruction Model Based on Generalized Approximate Message Passing

Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of the model by introducing sparsity, that is, retaining only some important features of the image. However, the...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 73; S. 1839 - 1850
Hauptverfasser: Jin, Wenzhe, Lyu, Wentao, Guo, Qing, Deng, Zhijiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Accuracy
Approximation algorithms
Bayes methods
block coordinate descent
Computational modeling
Data models
generalized approximate message passing
Image reconstruction
Inference algorithms
Message passing
Signal processing algorithms
Sparse Bayesian learning
Sparse matrices
ISSN:1053-587X, 1941-0476
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Zusammenfassung:Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of the model by introducing sparsity, that is, retaining only some important features of the image. However, the traditional Sparse Bayesian Learning algorithm involves the operation of n<inline-formula><tex-math notation="LaTeX">\boldsymbol{\times}</tex-math></inline-formula>n dimensional matrix inversion during iterative update, which seriously affects the efficiency and speed of image reconstruction. In order to overcome the above defects, in this paper, we propose a fast robust Sparse Bayesian Learning image reconstruction model based on generalized approximate message passing (GAMP-FRSBL). The damped Gaussian generalized approximate message passing algorithm (Damped GGAMP) is introduced on the basis of SBL to avoid the matrix inversion problem. Combined with the convex optimization strategy, the block coordinate descent (BCD) method is used to iteratively update the parameters to improve the reconstruction efficiency of the model. Finally, experiments are conducted on Indor and Mondrian images, DOTA, COCO and UCM datasets to verify the effectiveness of the GAMP-FRSBL in image reconstruction.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2025.3566404