69 citations · 105 across the 23 of their papers we have counts for
25 papers
Two-Dimensional Tomographic Reconstruction From Projections With Unknown Angles and Unknown Spatial Shifts
Shreyas Jayant Grampurohit, Satish Mulleti, Ajit Rajwade
In parallel beam computed tomography (CT), an object is reconstructed from a series of projections taken at different angles. However, in some industrial and biomedical imaging app…
Verifiable Deep Quantitative Group Testing
Shreyas Jayant Grampurohit, Satish Mulleti, Ajit Rajwade
We present a neural network-based framework for solving the quantitative group testing (QGT) problem that achieves both high decoding accuracy and structural verifiability. In QGT,…
Compressive Recovery of Signals Defined on Perturbed Graphs
Sabyasachi Ghosh, Ajit Rajwade
Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks…
Unlabelled Sensing with Priors: Algorithm and Bounds
Garweet Sresth, Ajit Rajwade, Satish Mulleti
In this study, we consider a variant of unlabelled sensing where the measurements are sparsely permuted, and additionally, a few correspondences are known. We present an estimator…
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing
Sabyasachi Ghosh, Sanyam Saxena, Ajit Rajwade
Popular social media platforms employ neural network based image moderation engines to classify images uploaded on them as having potentially objectionable content. Such moderation…
Performance Bounds for LASSO under Multiplicative Noise: Applications to Pooled RT-PCR Testing
Richeek Das, Aaron Jerry Ninan, Adithya Bhaskar +1
Group testing is a technique which avoids individually testing samples for a rare disease and instead tests pools, where a pool consists of a mixture of small, equal po…
Group Testing with Side Information via Generalized Approximate Message Passing
Shu-Jie Cao, Ritesh Goenka, Chau-Wai Wong +2
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each indiv…
A Weighted Generalized Coherence Approach for Sensing Matrix Design
Ameya Anjarlekar, Ajit Rajwade
As compared to using randomly generated sensing matrices, optimizing the sensing matrix w.r.t. a carefully designed criterion is known to lead to better quality signal recovery giv…
Contact Tracing Information Improves the Performance of Group Testing Algorithms
Ritesh Goenka, Shu-Jie Cao, Chau-Wai Wong +2
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In group testing, we are given samples, one per individual. These samples ar…
Analyzing Cross Validation In Compressed Sensing With Mixed Gaussian And Impulse Measurement Noise With L1 Errors
Chinmay Gurjarpadhye, Shubhang Bhatnagar, Ajit Rajwade
Compressed sensing (CS) involves sampling signals at rates less than their Nyquist rates and attempting to reconstruct them after sample acquisition. Most such algorithms have para…
Reconstruction of Sparse Signals under Gaussian Noise and Saturation
Shuvayan Banerjee, Radhe Srivastava, Ajit Rajwade
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 dynami…
Contact Tracing Enhances the Efficiency of COVID-19 Group Testing
Ritesh Goenka, Shu-Jie Cao, Chau-Wai Wong +2
Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given samples, one per individual, and arrange them into $m <…
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan +9
We propose `Tapestry', a novel approach to pooled testing with application to COVID-19 testing with quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can r…
Low radiation tomographic reconstruction with and without template information
Preeti Gopal, Sharat Chandran, Imants Svalbe +1
Low-dose tomography is highly preferred in medical procedures for its reduced radiation risk when compared to standard-dose Computed Tomography (CT). However, the lower the intensi…
Tomographic reconstruction to detect evolving structures
Preeti Gopal, Sharat Chandran, Imants Svalbe +1
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, i…
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image Representations
Jerin Geo James, Pranay Agrawal, Ajit Rajwade
Images of static scenes submerged beneath a wavy water surface exhibit severe non-rigid distortions. The physics of water flow suggests that water surfaces possess spatio-temporal…
Noise- and Outlier-Resistant Tomographic Reconstruction under Unknown Viewing Parameters
Ritwick Chaudhry, Arunabh Ghosh, Ajit Rajwade
In this paper, we present an algorithm for effectively reconstructing an object from a set of its tomographic projections without any knowledge of the viewing directions or any pri…
Learning from past scans: Tomographic reconstruction to detect new structures
Preeti Gopal, Sharat Chandran, Imants Svalbe +1
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, p…
Noise- and Outlier-Resistant Tomographic Reconstruction under Unknown Viewing Parameters
Ritwick Chaudhry, Arunabh Ghosh, Ajit Rajwade
In this paper, we present an algorithm for effectively reconstructing an object from a set of its tomographic projections without any knowledge of the viewing directions or any pri…
Tomographic Reconstruction using Global Statistical Prior
Preeti Gopal, Ritwick Chaudhry, Sharat Chandran +2
Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the incr…
Signal Recovery in Perturbed Fourier Compressed Sensing
Eeshan Malhotra, Himanshu Pandotra, Ajit Rajwade +1
In many applications in compressed sensing, the measurement matrix is a Fourier matrix, i.e., it measures the Fourier transform of the underlying signal at some specified `base' fr…
Optimizing Matrices For Compressed Sensing Using Existing Goodness Measures: Negative Results, And An Alternative
Alankar Kotwal, Ajit Rajwade
The bound that arises out of sparse recovery analysis in compressed sensing involves input signal sparsity and some property of the sensing matrix. An effort has therefore been mad…
Reconstruction Error Bounds for Compressed Sensing under Poisson or Poisson-Gaussian Noise Using Variance Stabilization Transforms
Deepak Garg, Pakshal Bohra, Karthik S. Gurumoorthy +1
Most existing bounds for signal reconstruction from compressive measurements make the assumption of additive signal-independent noise. However in many compressive imaging systems,…
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery
Alankar Kotwal, Ajit Rajwade
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 di…