Equivalence principle for optimization of sparse versus low-spread representations for signal estimation in noise.
Radu V. BalanJustinian RoscaScott RickardPublished in: Int. J. Imaging Syst. Technol. (2005)
Keyphrases
- low signal to noise ratio
- random noise
- signal subspace
- signal reconstruction
- compressive sensing
- signal noise ratio
- additive noise
- signal recovery
- signal detection
- wide band
- signal processing
- white noise
- received signal
- low snr
- high noise
- noise level
- noisy environments
- response function
- additive gaussian noise
- estimation error
- signal to noise ratio
- sparse representation
- optimization problems
- low frequency
- high frequency
- compressive sampling
- stochastic resonance
- dense motion estimation
- multiplicative noise
- noise model
- missing data
- impulse response
- wiener filter
- joint optimization
- high dimension
- power spectrum
- noisy data
- biomedical signals
- frequency domain
- image restoration
- maximum likelihood
- weak signal
- optical flow