1From Denoising to Compressed Sensing
Rice University · Columbia University
Abstract
A denoising algorithm seeks to remove perturbations or errors from a signal. The last three decades have seen extensive research devoted to this arena, and as a result, today’s denoisers are highly optimized algorithms that effectively remove large amounts of additive white Gaussian noise. A compressive sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively employ a generic denoiser in a CS reconstruction algorithm? In response, in this paper, we develop a…
Citation impact
- FWCI
- 66.97
- Percentile
- 100%
- References
- 81
Authors
3Topics & keywords
- Compressed sensing
- Additive white Gaussian noise
- Noise reduction
- Computer science
- Message passing
- Algorithm
- Noise (video)
- Signal reconstruction