Residual Noise Variance Research Articles

Channel Impulsive Noise Mitigation for Linear Video Coding Schemes

This paper considers the problem of impulse noise mitigation when video is encoded using a SoftCast-based Linear Video Coding (LVC) scheme and transmitted using an Orthogonal Frequency-Division Multiplexing (OFDM) scheme for multi-carrier modulation over a wideband channel prone to impulse noise. In the time domain, the impulse noise is modeled as realization of a sequence of independent and identically distributed Bernoulli-Gaussian variables. A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation. This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes. Provisioned subchannels cannot be used to transmit data and lead to a decrease of the video quality at receivers in absence of impulse noise. Using a phenomenological model (PM) of the residual noise variance after impulse mitigation in the subchannels, we have proposed an algorithms that is able to get the amount of subchannel to provision which minimizes the mean-square error of the decoded video at receivers. Simulation results show that the PM can accurately predict the number of subchannels to provision and that impulse noise mitigation can significantly improve the decoded video quality compared to a situation where all subchannels are used for data transmission.

A multiscale filter for noise reduction of low-dose cone beam projections

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of , which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024 × 768 pixels.

Removal of Mixed Poisson and Impulse Noise in 1-D Signals by Non-Adaptive and Adaptive Nonlinear Filters

A case of filtering 1-D signals corrupted by Poisson fluctuative and impulse noise is considered. Statistical characteristics of output signals for several conventional nonlinear filters are analyzed. It is shown that output bias can be present and it depends upon filter scanning window size and an information content of a processed signal. Residual noise variance is also studied. A locally adaptive filter that takes into account the observed statistics is proposed and tested.

Instability of Prefrontal Signal Processing in Schizophrenia

Prefrontal dysfunction is considered a fundamental characteristic of schizophrenia. Recent electrophysiological evidence points to a major instability of signal processing in prefrontal cortical microcircuits because of reduced phase-synchronization (i.e., an increased stimulus-related variability [noise] of single-trial responses in the spatial and time domain). The authors used functional magnetic resonance imaging (fMRI) during a visual two-choice reaction task in order to measure, with higher topographic accuracy, signal stability in patients with schizophrenia and its relationship to more traditional measures of activation. Twelve clinically stable inpatients with schizophrenia and 16 matched comparison subjects were evaluated. Event-related blood-oxygen-level-dependent responses were subjected to an analysis of residual noise variance and to independent data dimension independent component analysis in the medial prefrontal cortex. In patients with schizophrenia, the authors found increased residual noise variance of the blood-oxygen-level-dependent response that predicted the level of prefrontal activation in these subjects. In the left hemisphere, residual noise variance strongly correlated with psychotic symptoms. Independent component analysis revealed a "fractionized" and unfocussed pattern of activation in patients. These findings suggest that unstable cortical signal processing underlies classic abnormal cortical activation patterns as well as psychosis in schizophrenia.

Instability of Prefrontal Signal Processing in Schizophrenia

Objective: Prefrontal dysfunction is considered a fundamental characteristic of schizophrenia. Recent electrophysiological evidence points to a major instability of signal processing in prefrontal cortical microcircuits because of reduced phase-synchronization (i.e., an increased stimulus-related variability [noise] of single-trial responses in the spatial and time domain). The authors used functional magnetic resonance imaging (fMRI) during a visual two-choice reaction task in order to measure, with higher topographic accuracy, signal stability in patients with schizophrenia and its relationship to more traditional measures of activation. Method: Twelve clinically stable inpatients with schizophrenia and 16 matched comparison subjects were evaluated. Event-related blood-oxygen-level-dependent responses were subjected to an analysis of residual noise variance and to independent data dimension independent component analysis in the medial prefrontal cortex. Results: In patients with schizophrenia, the authors found increased residual noise variance of the blood-oxygen-level-dependent response that predicted the level of prefrontal activation in these subjects. In the left hemisphere, residual noise variance strongly correlated with psychotic symptoms. Independent component analysis revealed a “fractionized” and unfocussed pattern of activation in patients. Conclusions: These findings suggest that unstable cortical signal processing underlies classic abnormal cortical activation patterns as well as psychosis in schizophrenia.

COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits

Objective: Prefrontal dopamine (DA) is catabolized by the COMT (catechol- O-methyltransferase) enzyme. Literature suggests that the Val/Met single nucleotide polymorphism (SNP) in the COMT gene predicts executive cognition in humans with Val carriers showing poorer performance due to less available synaptic DA. Recent fMRI studies are thought to agree with these studies having demonstrated prefrontal hyperactivation during n-back and attention-requiring tasks. This was interpreted as “less efficient” processing due to impaired signal-to-noise ratio (SNR) of neuronal activity. However, electrophysiological studies of neuronal SNR in primates and humans imply that prefrontal cortex should show a diminished prefrontal BOLD response in Val carriers. In the present study, we addressed the question of whether the prefrontal SNR of the BOLD response is decreased in Val carriers using a visual oddball task and an approach to analysis of fMRI data that maximizes noise characterization. Methods: We investigated N = 17 homozygous Met carriers compared with N = 24 Val carriers matched for age, sex, education, IQ, reaction time (variability) and head motion. Event-related fMRI was conducted presenting 160 visual stimuli (40 targets, checkerboard reversal). Subjects had to respond as quickly as possible to targets by button press. In the fMRI GLM [ y( t) = β * x( t) + c + e( t)] analysis, voxel-by-voxel ‘activation’ [ y( t)] as well as residual noise variance [ e( t) = σ 2] were calculated using a conservative full-width half maximum (FWHM = 6 mm). Results: As compared to Val carriers, we observed a stronger and more extended BOLD responses in homozygous Met carriers in left supplementary motor area (SMA) extending to ACC and dorsolateral prefrontal cortex. Vice versa, increased levels of noise were seen in Val carriers surrounding the peak activation maximum. Discussion: In line with our expectations from prior electrophysiological studies, we observed a diminished BOLD response and increased noise in Val carriers. This suggests that the DA stabilizes cortical microcircuits by sharpening the signal and suppressing surrounding noise.

Convergence of an adaptive filter with signed filtered error

The need for high-speed adaptive filters has prompted the search for alternatives to the popular LMS algorithm. One modification is the replacement of the prediction error term in the LMS update kernel by its signum function. At the same time, in noise and echo cancellation problems, reduced residual noise variance often requires error filtering. In the paper, the authors consider the situation where the signum function of such a filtered error appears in the update kernel. Without the signum function, the error model of the filtered algorithms is similar to those in output error identifiers, and a strictly positive real (SPR) error filter suffices for convergence. The authors analyze the signed filtered error algorithm to show that because of the sign operator a SPR error filter no longer guarantees convergence. A new concept in systems theory, namely, that of strictly dominant passivity (SDP) is introduced instead. A system is SDP if the average product of its output and the signum of the input is positive. The authors conduct a systems theoretic investigation of the SDP condition and prove that the algorithm under study is convergent if the inverse of the error filter is SDP. >