Accelerated Alternating Minimization for X-ray Tomographic Reconstruction

Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization

We propose a model-based image reconstruction method for photoacoustic tomography(PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constr…

Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring

In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing. We solve this problem by the penalty method and propose the iteratively reweighted alternating minimization algorithm. To speed up the algorithm, we also apply the continuation strategy to the penalty parameter. A convergence result is proved for the algorithm. Compared with the nonconvex ADMM, the proposed algorithm enjoys both theoretical and computational advantages like weaker convergence requirements and faster speed. Numerical results demonstrate the effi…

基于L+S交替梯度下降与最小化的低延迟高泛化性动态MRI方法

Low Latency and Generalizable Dynamic MRI via L+S Alternating GD and Minimization

本研究针对动态MRI重建需求，提出基于低秩加稀疏（L+S）模型的少参数交替梯度下降与最小化方法，无需问题特定调参即可在多场景下实现准确、低延迟的重建，验证了简单少参数模型与算法的高泛化性。

PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm with Application to Separate Regularization of Magnitude and Phase

We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many different image reconstruction contexts, although is nonconvex and can be difficult to solve. In this work, we first describe a novel implementation of the previous proximal alternating linearized minization (PALM) algorithm to solve this optimization problem. We then make enhancements to PALM, leading to a new algorithm named PALMNUT that combines the PALM together with Nesterov's momentum and a novel approach that relies…

Inertial nonconvex alternating minimizations for the image deblurring

In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images. One of the most efficient and popular methods to solve the convex TV problem is the Alternating Direction Method of Multipliers (ADMM) algorithm, recently extended using the inertial proximal point method. Although all the classical studies focus on only a convex formulation, recent articles are paying increasing attention to the nonconvex methodology due to its good numerical performance and properties. In this paper, we propose to extend the classical formulation with a novel nonconve…

基于稀疏性与深度图像先验的编码孔径快照光谱成像快速交替最小化算法

A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors

针对CASSI高光谱重建在少测量、多通道下的不适定问题，提出融合稀疏性与DIP的无训练快速交替最小化算法Fama-SDIP，并在仿真和真实数据集上取得领先性能。