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Found 8 papers, 2 papers with code
Pushing the Limit of Range Resolution Beyond Bandwidth Constraint with Triangle FMCW
This paper proposes a novel signal processing technique that doubles the range resolution of FMCW~(Frequency Modulated Continuous Wave) sensing without increasing the required bandwidth.
A Relational Macrostate Theory Guides Artificial Intelligence to Learn Macro and Design Micro
By demonstrating a general theory for how macroscopic properties emerge from conservation of symmetries in the mapping between observations, we provide a machine learning framework that allows a unified approach to identifying macrostates in systems from the simple to complex, and allows the design of new examples consistent with a given macroscopic property.
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction
Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
Metal artifact reduction based on the combined prior image
The results of three patients' CT images show that the proposed method can reduce metal artifacts remarkably.
Medical Physics
