We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sens...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
Abstract--This paper describes performance bounds for compressed sensing (CS) where the underlying sparse or compressible (sparsely approximable) signal is a vector of nonnegative ...
Maxim Raginsky, Rebecca Willett, Zachary T. Harman...
In this paper we introduce an information theoretic approach and use techniques from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a s...
This paper presents a new technique for rendering bidirectional texture functions (BTFs) at different levels of detail (LODs). Our method first decomposes each BTF image into mul...