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Found 27 papers, 10 papers with code
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking
Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions.
Spline Sketches: An Efficient Approach for Photon Counting Lidar
Photon counting lidar has become an invaluable tool for 3D depth imaging due to the fine-precision it can achieve over long ranges.
Adaptive Kernel Kalman Filter
With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs).
Sketched RT3D: How to reconstruct billions of photons per second
Single-photon light detection and ranging (lidar) captures depth and intensity information of a 3D scene.
Deep Unrolling for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach.
Magnetic Resonance Fingerprinting
Rolling Shutter Correction
Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography.
Compressive Independent Component Analysis: Theory and Algorithms
Compressive learning forms the exciting intersection between compressed sensing and statistical learning where one exploits forms of sparsity and structure to reduce the memory and/or computational complexity of the learning task.
Surface Detection for Sketched Single Photon Lidar
The computational load of the proposed detection algorithm depends solely on the size of the sketch, in contrast to previous algorithms that depend at least linearly in the number of collected photons or histogram bins, paving the way for fast, accurate and memory efficient lidar estimation.
Equivariant Imaging: Learning Beyond the Range Space
In various imaging problems, we only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signals and associated measurements for training.
A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar
Single-photon lidar has become a prominent tool for depth imaging in recent years.
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement.
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results.
DeepMP for Non-Negative Sparse Decomposition
Non-negative signals form an important class of sparse signals.
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction
Non-negative signals form an important class of sparse signals.
Deep Decomposition Learning for Inverse Imaging Problems
Deep learning is emerging as a new paradigm for solving inverse imaging problems.
Compressive Learning for Semi-Parametric Models
In the compressive learning theory, instead of solving a statistical learning problem from the input data, a so-called sketch is computed from the data prior to learning.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses.
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image processing.
CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery
Current proposed solutions for the high dimensionality of the MRF reconstruction problem rely on a linear compression step to reduce the matching computations and boost the efficiency of fast but non-scalable searching schemes such as the KD-trees.
Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF
The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i. e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF.
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine
We investigate the potential of a restricted Boltzmann Machine (RBM) for discriminative representation learning.
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss
Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent.
Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery
We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds.
Sample Distortion for Compressed Imaging
We propose the notion of a sample distortion (SD) function for independent and identically distributed (i. i. d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder pairs at a given sampling ratio.
Iterative Hard Thresholding for Compressed Sensing
- It requires a fixed number of iterations depending only on the logarithm of a form of signal to noise ratio of the signal.
Information Theory Numerical Analysis Information Theory Numerical Analysis
