Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal
information in imaging data), a self-supervised learning method for removing Poisson …

The traditional K‐singular value decomposition (K‐SVD) algorithm has poor image‐
denoising performance under strong noise. An image‐denoising algorithm is proposed …

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue
in machine learning. Identifying anomalous objects from given data has a broad range of …

Abstract At most 1-2% of the global virome has been sampled to date. Here, we develop a
novel method that combines Linear Filtering (LF) and Singular Value Decomposition (SVD) …

Image textures, as a kind of local variations, provide important information for the human
visual system. Many image textures, especially the small-scale or stochastic textures, are …

Because there are many similar geologic structures underground, seismic profiles have an
abundance of self-repeating patterns. Thus, we can divide a seismic profile into groups of …

An image denoising method is proposed for ultrasonic logging images with severe noise.
The proposed method works on a variational Bayesian framework using block sparse prior …

In this paper, we propose a new convolutional neural network (CNN) architecture for
improving document-image quality through decreasing the impact of distortions (ie, blur …

Predicting host-virus interactions is fundamentally a network science problem. We develop a
method for bipartite network prediction that combines a recommender system (linear …

We consider the edge statistics of large dimensional deformed rectangular matrices of the
form Y t= Y+ t X, where Y is ap× n deterministic signal matrix whose rank is comparable to n …