Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM their superior sensitivity, resolution, field of view, frame rates. However, the unique single-pixel-dependent readout noise may lead reconstruction artefacts affect accuracy subsequent statistical analysis. We first established a nonuni...

Many established and emerging image processing applications rely on quantum-limited imaging, i.e., imaging in extremely poor illumination. At this, images are corrupted by severe signal-dependent Poisson noise. For optimal noise reduction the noise characteristics must be estimated and integrated into the method. Common noise estimators, however, assume Gaussian noise which is not signal-depend...

Maximum and near-maximum likelihood sequence detectors in signal-dependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signal-dependent Markov noise. The resulting detector architecture is viewed as a noise predictive maximum likelihood detector that operates o...

The Bayesian Cramér Rao Bound (BCRB) is derived for nonlinear state space models with dependent process and measurement noise processes. It generalizes the previously BCRB for the case of dependent noise. Two different dependence structures appearing in literature are considered, leading to two different recursions for BCRB. The special cases of Gaussian noise, and linear models are presented s...

Tra c using the Fractal-Shot-Noise-Driven Poisson Process Bong K. Ryu and Steven B. Lowen Department of Electrical Engineering and Center for Telecommunications Research Columbia University, New York, NY 10027, U.S.A. fryu, [email protected] Abstract Presented in this paper is the fractal-shot-noisedriven Poisson process (FSNDP), a self-similar process which permits parsimonious modeling...

The separation of signals from noisy vector measurements is obtained by taking advantage of the Middleton Class A model of noise amplitude and the correlation of the components of the noise process due to their polarization. The signal is assumed to be white Gaussian. Noise is a superposition ofM non-Gaussian processes, each with a fixed azimuth of polarization. Neither the number of processes ...

Noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of  the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt an...