Noise, which corresponds to random variations extrinsic to the signals of interest, is an ubiquitous aspect that affects the performance of many tasks in signal processing. Even with the improving quality and sophistication of the modern acquisition devices, digital signals still carry noise due to many incontrollable factors. This research focuses on the fundamental problem of estimating parameters of the random noise model directly from a noise corrupted signal. As an immediate consequence, the results of this investigation will be applicable in a wide range of fields, including the forensic analysis of digital images, automatic processing of medical images, spectrum sensing in wireless communications and data processing in sensory neuroscience.
The technical approach taken in this research exploits the regular statistical properties of the original signals in multiple signal representations and their relationship with the noise parameters. Specifically, we will investigate the use of domains constructed from random band-pass filters that are more effective in revealing ?typical? statistical properties of the signals, especially when compared with deterministic representations such as Fourier, DCT, and wavelet. Concurrently, we will investigate the mathematical relationship between the observed statistics of noisy signal and the noise parameters. Drawing on these theoretical findings, this research is expected to lead to more effective and efficient algorithms for blind noise estimation. More generally, the proposed work will also explore efficient algorithms for blind local noise estimation in the presence of non-stationary noise statistics.
