6 accepted papers
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman Filter, Particle Filter) presuppose full knowledge of the true dynamics, which is not always satisfied in practice, while purely data-driven solvers le…
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding memory, time, and data requirements, it is difficult to train a diffusion model directly on the entire volume of high-dim…
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and requires lots of data. Such bottlenecks prevent most existing works from being feasible for high-dimensional and high-res…
Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data samples in each mixture contain homoscedastic noise. However, datasets with heterogeneous noise across samples are beco…
Light-field (LF) reconstruction from focal stack images has diverse applications including face recognition, autonomous driving, and 3D reconstruction in virtual reality. It is a large-scale ill-conditioned inverse problem and typically requires regularized iterative algorithms to solve, which can b…
We study accelerated dual gradient-based methods for image denoising/deblurring problems based on the total variation (TV) model. For the TV-based denoising problem, combining the dual approach and Nesterov's fast gradient projection (FGP) method has been found effective. The corresponding denoising…