In non-Cartesian SENSE reconstruction based on the conjugate gradient (CG) iteration method, the iteration very often exhibits a "semi-convergence" behavior, which can be characterized as initial convergence toward the exact solution and later divergence. This phenomenon causes difficulties in automatic implementation of this reconstruction strategy. In this study, the convergence behavior of t...

Image de-blurring is important in many cases of imaging a real scene or object by a camera. This project focuses on de-blurring an image distorted by an out-of-focus blur through a simulation study. A pseudo-inverse filter is first explored but it fails because of severe noise amplification. Then Tikhonov regularization methods are employed, which produce greatly improved results compared to th...

Manifold regularization, which learns from a limited number of labeled samples and a large number of unlabeled samples, is a powerful semi-supervised classifier with a solid theoretical foundation. However, manifold regularization has the tendency to misclassify data near the boundaries of different classes during the classification process. In this paper, we propose a novel classification meth...

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For met...

In subspace identification methods, the system matrices are usually estimated by least squares, based on estimated Kalman filter state sequences and the observed inputs and outputs. For a finite number of data points, the estimated system matrix is not guaranteed to be stable, even when the true linear system is known to be stable. In this note, stability is imposed by using regularization. The...

Iterative regularization multigrid methods have been successful applied to signal/image deblurring problems. When zero-Dirichlet boundary conditions are imposed the deblurring has a Toeplitz structure and it is potentially full. A crucial task of a multilevel strategy is to preserve the Toeplitz structure at the coarse levels which can be exploited to obtain fast computations. The smoother has ...

Metallographic image segmentation is a core task towards the automation of metallographic analysis. Currently, most advanced methods for this generally employ supervised deep learning models that require great number pixel-level annotated images, while annotation process time-consuming and labor-intensive. In order to address issue, semi-supervised model called Con2Net proposed in work, which l...

BACKGROUND Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. METHODS We present a semi-automated framework for brain tumor segmentatio...

We propose two generic methods for improving semi-supervised learning (SSL). The first integrates weight perturbation (WP) into existing “consistency regularization” (CR) based methods. implement WP by leveraging variational Bayesian inference (VBI). second method proposes a novel consistency loss called “maximum uncertainty (MUR). While most losses act on perturbations in the vicinity of each ...