Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. A simultaneous Bayesian framework is extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation is proposed with transformation analysis, which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting are embedded into the framework to achieve rapid convergence and high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis are embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared with conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that the proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyze the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denoising.

中文翻译：

---

嵌入贝叶斯框架的带有变换分析的结构化逼近的同时稀疏学习算法

稀疏逼近对于信号或图像处理的应用至关重要，它有利于与变换分析的共同努力估计稀疏信号。通过结构化共享支持为稀疏逼近扩展了同时贝叶斯框架，并提出了一种结构化逼近的同时稀疏学习算法，结合变换分析，得到更合理的可行解。然后将稀疏贝叶斯学习和迭代重新加权的改进嵌入到框架中，以实现快速收敛和高效率且具有鲁棒性。此外，迭代优化和转换分析嵌入到整个学习过程中，以获得稀疏近似的相对最优值。最后，与具有l1和l2的同时稀疏模型的传统重加权算法相比，仿真结果表明所提出的方法在解决稀疏信号处理中的稀疏结构和迭代冗余方面具有优势。事实表明，所提出的方法将有效地对各种信号和图像进行稀疏近似，从而在优化变换中准确地分析目标。设想所提出的模型可以适用于稀疏分离和信号去噪中的广泛数据。事实表明，所提出的方法将有效地对各种信号和图像进行稀疏近似，从而在优化变换中准确地分析目标。设想所提出的模型可以适用于稀疏分离和信号去噪中的广泛数据。事实表明，所提出的方法将有效地对各种信号和图像进行稀疏近似，从而在优化变换中准确地分析目标。设想所提出的模型可以适用于稀疏分离和信号去噪中的广泛数据。