Publications (5)
Robust Compressed Sensing and Sparse Coding with the Difference Map
Will Landecker, Rick Chartrand, Simon DeDeo
In compressed sensing, we wish to reconstruct a sparse signal from observed data . In sparse coding, on the other hand, we wish to find a representation of an observed signa…
BUDD: Multi-modal Bayesian Updating Deforestation Detections
Alice M. S Durieux, Christopher X. Ren, Matthew T. Calef +2
The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updati…
Compressed Sensing Recovery via Nonconvex Shrinkage Penalties
Joseph Woodworth, Rick Chartrand
The minimization of compressed sensing is often relaxed to , which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown…
Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery
Michael S. Warren, Samuel W. Skillman, Rick Chartrand +4
We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in high-performan…
High resolution image reconstruction with constrained, total-variation minimization
Emil Y. Sidky, Rick Chartrand, Yuval Duchin +2
This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling…