This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f ∈ C N and a randomly chosen set of freque...
Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rat...
This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e....
Abstract Suppose we wish to recover a vector x 0 ∈ ℝ 𝓂 (e.g., a digital signal or image) from incomplete and contaminated observations y = A x 0 + e ; A is an 𝓃 × 𝓂 matrix wi...
Suppose we are given a vector f in a class F ⊂ ℝN, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to reco...
We consider the problem of reconstructing a sparse signal x^0\\in{\\bb R}^n from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an...
Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This p...
We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact re...
h-index: Number of publications with at least h citations each.