Adaptive Block Sparse Regularization under Arbitrary Linear Transform
We propose a convex and fast signal reconstruction method for block sparsity under arbitrary linear transform with unknown block structure. The proposed method is a generalization of the similar existing method and can reconstruct signals with block sparsity under non-invertible transforms, unlike the existing method. Our work broadens the scope of block sparse regularization, enabling more versatile and powerful applications across various signal processing domains. We derive an iterative algorithm for solving proposed method and provide conditions for its convergence to the optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Combined Sparse Regularization for Nonlinear Adaptive Filters
Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been pro…
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, …
Representation LearningAdaptive and Self-Tuning SBL with Total Variation Priors for Block-Sparse Signal Recovery
This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV)…
Optimization and Regularization Under Arbitrary Objectives
This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between Metropolis-Hastings and Gibbs …
Reinforcement LearningLearned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which…
Super-Resolution