论文标题
广义稀疏的贝叶斯学习和图像重建的应用
Generalized sparse Bayesian learning and application to image reconstruction
论文作者
论文摘要
基于间接，嘈杂或不完整数据的图像重建仍然是一项重要但具有挑战性的任务。尽管诸如压缩传感之类的方法已显示出在各种设置中的高分辨率图像恢复，但由于参数调整而存在鲁棒性的问题。此外，由于恢复仅限于点估计值，因此不可能量化不确定性，这通常是可取的。由于这些固有的局限性，有时采用稀疏的贝叶斯学习方法来恢复未知的后验分布。稀疏的贝叶斯学习假设未知的某些线性转化是稀疏的。但是，开发的大多数方法都是针对特定问题，特定的远期模型和先验量身定制的。在这里，我们提出了一种稀疏贝叶斯学习的通用方法。它具有一个优势，可以用于各种类型的数据采集和先验信息。图像重建/恢复的一些初步结果表明其潜在用于降解，脱毛和磁共振成像。
Image reconstruction based on indirect, noisy, or incomplete data remains an important yet challenging task. While methods such as compressive sensing have demonstrated high-resolution image recovery in various settings, there remain issues of robustness due to parameter tuning. Moreover, since the recovery is limited to a point estimate, it is impossible to quantify the uncertainty, which is often desirable. Due to these inherent limitations, a sparse Bayesian learning approach is sometimes adopted to recover a posterior distribution of the unknown. Sparse Bayesian learning assumes that some linear transformation of the unknown is sparse. However, most of the methods developed are tailored to specific problems, with particular forward models and priors. Here, we present a generalized approach to sparse Bayesian learning. It has the advantage that it can be used for various types of data acquisitions and prior information. Some preliminary results on image reconstruction/recovery indicate its potential use for denoising, deblurring, and magnetic resonance imaging.
