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Found 9 papers, 0 papers with code
Towards Quantum Tensor Decomposition in Biomedical Applications
Our study includes a preliminary resource estimation analysis for quantum computing platforms and examines the feasibility of implementing quantum-enhanced tensor decomposition methods on near-term quantum devices.
Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation
The segmentation of lung nodules plays a critical role in aiding physicians in distinguishing between malignant and benign lesions.
Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness
Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies.
A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries
For instance, Magnetic Resonance (MR) muscle images often contain both of these issues, making muscle segmentation especially difficult.
Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model
The main objective of image segmentation is to divide an image into homogeneous regions for further analysis.
Image Segmentation with Adaptive Spatial Priors from Joint Registration
In medical imaging for instance, intensity inhomogeneity and noise are common.
PCM-TV-TFV: A Novel Two Stage Framework for Image Reconstruction from Fourier Data
We propose in this paper a novel two-stage Projection Correction Modeling (PCM) framework for image reconstruction from (non-uniform) Fourier measurements.
Single image super-resolution by approximated Heaviside functions
We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively.
Two-stage Geometric Information Guided Image Reconstruction
Existing methods mostly work well on piecewise constant images but not so well on piecewise smooth images such as natural images, medical images that contain a lot of details.
