Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multi-lAyer Residual Sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using Penalized Weighted Least Squares (PWLS) optimization. We propose new formulations for multi-layer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase. Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as FBP and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details.
翻译:近些年来,基于鲜少的表示方式的信号模型受到相当的重视。另一方面,由功能层级级级级级级级级级级的深层模型(通常称为深神经网络)在物体分类任务方面非常成功,最近引入了图像重建。在这项工作中,我们根据通过将稀少的表示方式和深层模型结合起来而以不受监督的方式学习的新多层次模型,开发了一种新的图像重建方法。拟议框架将传统的图像转换模型扩展为多层次级级级的SSS(MARS)模型,其中变异的域域数据在层间联合封闭。我们调查了MARS模型在低剂量CT重建中从有限的定期剂量图像中学习的模型应用情况,最近还引入了图像重建。我们提出了一种新的多层次变异学习和图像重建新模式的新公式。我们从一个高效的缩影算法,从有限的常规版级级级变异图像维持(MARS)在低剂量模型重建阶段化模型阶段联合进行联合。我们调查了MIM-ST的常规S(5-RBS)的定期测试结果,在IM-RM-RI-S 和MLBS的S中以常规的S级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级级的模型中,在S进行。5-MMMM-M-级级级的S的S的S的常规数据中,在S级级级级级级级级的S级级级级级级级级的S级的S级的S级的S级级的常规级的S级中,在S-级中,在5-级的基级级级级级级级级级级级级级级级级级级级的常规级的S级中,在5-级的基级的SBS级中,在SBSBS级中,在S-级级级级级级级级级级级级级级的S级级级级级级级级级级级级的S级级级级级级级级级级级级级级级级级级级级级级的S级的基级的S级上,在