Search Results for author:
Found 20 papers, 3 papers with code
Compressive Recovery of Signals Defined on Perturbed Graphs
This impedes recovery of signals which may have sparse representations in the GFT bases of the ground truth graph.
Group Testing for Accurate and Efficient Range-Based Near Neighbor Search : An Adaptive Binary Splitting Approach
Unlike other methods, it does not assume a large difference between the cosine similarity of the query vector with the least related neighbor and that with the least unrelated non-neighbor.
Unlabelled Sensing with Priors: Algorithm and Bounds
We present an estimator to solve for the unknown vector.
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing
The computational cost of running the QMPNN and the CS algorithms is significantly lower than the cost of using a neural network with the same number of parameters separately on each image to classify the images, which we demonstrate via extensive experiments.
Estimating Joint Probability Distribution With Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries
In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying distribution can be decomposed as a mixture of product densities with few mixture components.
Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles
More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample locations and their ordering in 1D.
Group Testing with Side Information via Generalized Approximate Message Passing
These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples.
GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates
However, in this work, we introduce a more general representation scheme, which adapts any existing optical flow network to ignore the moving objects and obtain a spatially smooth approximation of the global motion between video frames.
Joint Probability Estimation Using Tensor Decomposition and Dictionaries
We create a dictionary of various families of distributions by inspecting the data, and use it to approximate each decomposed factor of the product in the mixture.
A Weighted Generalized Coherence Approach for Sensing Matrix Design
As compared to using randomly generated sensing matrices, optimizing the sensing matrix w. r. t.
Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections
Joint probability mass function (PMF) estimation is a fundamental machine learning problem.
Reconstruction of Sparse Signals under Gaussian Noise and Saturation
Most compressed sensing algorithms do not account for the effect of saturation in noisy compressed measurements, though saturation is an important consequence of the limited dynamic range of existing sensors.
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data.
Low radiation tomographic reconstruction with and without template information
Our results on 3D data show that prior information can be used to compensate for the low-dose artefacts, and we demonstrate that it is possible to simultaneously prevent the prior from adversely biasing the reconstructions of new changes in the test object, via a method called ``re-irradiation''.
Tomographic reconstruction to detect evolving structures
While this is easily feasible when measurements are acquired from a large number of projection views, it is challenging when the number of views is limited.
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image Representations
Surprisingly, we demonstrate that PEOF is more efficient and often outperforms all the state of the art methods in terms of numerical measures.
Learning from past scans: Tomographic reconstruction to detect new structures
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical.
Tomographic Reconstruction using Global Statistical Prior
Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements.
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery
There exist several applications in image processing (eg: video compressed sensing [Hitomi, Y. et al, "Video from a single coded exposure photograph using a learned overcomplete dictionary"] and color image demosaicing [Moghadam, A.
Block and Group Regularized Sparse Modeling for Dictionary Learning
This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL) algorithm.
