基于贝叶斯压缩感知的光声图像重建算法
摘要
The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.
引用本文（GB/T 7714）
Mingjian Sun, Naizhang Feng, Yi Shen, 等. 基于贝叶斯压缩感知的光声图像重建算法[J]. Chinese Optics Letters, 2011.
引文网络
本站仅收录题录与摘要供学习参考，全文版权归属出版方；如有侵权请联系我们删除。