A Study on Distortion Estimation Based on Image Gradients

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.

Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.

The five-point non-coherent model of a distributed radar object providing the independent control of angle noise probability characteristics along two orthogonal coordinate axes

The two-dimensional five-point non-coherent model replacing a distributed radar target is explored in this work. Four fixed model points are set in corners of the square but the fifth movable point lies inside of this square. Model points are supplied by normal uncorrelated random processes. The possibilities of the five-point non-coherent model of a distributed radar object for independent control of the producing angle noise parameters along two orthogonal coordinate axes are explored. The disadvantage of this model is noted - the connection of parameters values of angle noise probability density function for two coordinate axes. The expression describing this connection is specified. Expressions determining the boundaries of the allowable coordinate values of the fifth movable point of the five-point non-coherent model, within which the model provides the set parameters of the angle noise probability density function, are defined. The arrived results are validated by program simulations.

Investigation influence of switching tests coatings of magnetically controlled contacts on the surface structure

The results of spectral noise power density measurements of reed switches 10 samples with and without Au-Ru coating are analyzed. The dependences of the spots area formed during switching tests on the electrodes on various noise parameters are constructed.

Stabilization of noise parameters during annealing of highly alloyed structures of noise diodes

Stabilization of residual point defects at the finishing stages of noise diodes can be ensured by the selection of appropriate ignition modes and the environment of its conduct. The method and technology of reducing the concentration of point defects in the structure of p-n-transition, taking into account the content of impurities of secondary metals, oxygen and nitrogen is proposed. It has been established that the burning of readymade structures and diodes of noise generators in nitrogen environments at temperatures of 450…600 °С for (80 ± 3) min leads to an increase in spectral noise density and a significant (twice) reduction of its unevenness. The most important result of this burn is a reduction in the spread of the average values of the noise parameters studied: by 61.2 % in effective noise voltage; spectral density by 34.2 %; at the boundary frequency of the signal by 34.9 %; in non-linear density by 25.9 %. This improves the quality of random numerical sequences in information protection software systems.

Traffic Noise Pollution Assessment in Major RoadJunctions of Imphal City, Manipur (India)

Noise pollution assessment was carried out in selected traffic junctions of Imphal city of Manipur, India. The noise pollution assessment was carried out using noise parameters and indices such as L10, L50, L90, Leq for selected traffic junctions during the different periods of the day, i.e., morning, noon, and evening hours. The study of equivalent noise level (Leq), noise parameters, and various noise indices have enabled the evaluation of the overall traffic noise environment of the city. The traffic noise indices such as traffic noise index (TNI), noise climate (NC), traffic noise pollution level (LNP), noise exposure index (NEI) along with day time (LD), night time (LN) average, and day-night (Ldn) noise levels were assessed for the selected traffic junctions. Moreover, spatial noise mapping was carried out using the geostatistical interpolation technique to evaluate the changes of traffic noise scenarios during the different time zones of the day. The Leq values in few traffic junctions exceeded the required noise standards. The study shows equivalent noise level ranging between 52.2–69.9 dB(A) during the morning (7–10 am), 52.4–69.3 dB(A) during noon (12 noon-2 pm), and 54.6–71.1 dB(A) during the evening (4–7 pm) hours, respectively.

A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.