Adaptive Noise Detection and Removal Algorithm Using Local Statistics for Salt-and-Pepper Noise

In this paper, an adaptive noise detection and removal algorithm using local statistics for salt-and-pepper noise are proposed. In order to determine constraints for noise detection, the local mean, varianoe, and maximum value are used. In addition, a weighted median filter is employed to remove the detected noise. The simulation results show the capability of the proposed algorithm removes the noise effectively.

Adaptive Gaussian Noise Image Removal Algorithm Using Filtering-Based Noise Estimation

This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise density.To adaptively deal with variety of the noise amount,a noisy input image is firstly filtered by a lowpass filter.Standard deviation of the noise is computed from different images between the noisy input and its filtered image.In addition,a modified Gaussian noise removal filter based on the local statistics such as local weighted mean,local weighted activity and local maximum is used to control the degree of noise suppression.Experiments show the effectiveness of the proposed algorithm.

Automatic estimation and removal of noise on digital image

An spatially adaptive noise detection and removal algorithm is proposed.Under the assumption that an observed image and its additive noise have Gaussian distribution,the noise parameters are estimated with local statistics from an observed degraded image,and the parameters are used to define the constraints on the noise detection process.In addition,an adaptive low-pass filter having a variable filter window defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image.Experimental results demonstrate the capability of the proposed algorithm.

Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

Identifying porphyry-Cu geochemical footprints using local neighborhood statistics in Baft area, Iran

Identifying geochemical anomalies related to ore deposition processes facilitates the practice of vectoring toward undiscovered mineral deposit sites.In districtscale exploration studies,analysis of dispersion patterns of ore-forming elements results in more-reliable targets.Therefore,deriving significant geochemical footprints and mapping the ensuing geochemical anomalies are of important issues that lead exploration geologists toward anomaly sources,e.g.,mineralization.This paper aims to examine the effectiveness of local relative enrichment index and singularity mapping technique,as two methods of local neighborhood statistics,in the delineation of anomalous areas for further exploration.A data set of element contents obtained from stream sediment samples in Baft area,Iran,therefore was applied to illustrate the procedure proposed.The close relationship between anomalous patterns recognized and known Cu-occurrences demonstrated that the procedures proposed can efficiently model complex dispersion patterns of geochemical anomalies in the study area.The results showed that singularity mapping method is a better technique,compared to local relative enrichment index,to delineate targets for follow-up exploration in the area.We made this comparison because,as pointed out by exploration geochemists,dispersion patterns of geochemical indicators in stream sediments vary in different areas even for the same deposit type.The variety in the dispersion patterns is due to the operation of post-mineralization subsystems,which are affected by local factors such as landscape of the areas under study.Therefore,the effectiveness of the methods should be evaluated in every area for every targeted deposit.

The vertical distribution characteristics of integral turbulence statistics in the atmospheric boundary layer over an urban area in Beijing

Turbulence data(2008–2012) from a 325 m meteorological tower in Beijing, which consisted of three layers(47,140, and 280 m), was used to analyze the vertical distribution characteristics of turbulent transfer over Beijing city according to similarity theory. The conclusions were as follows.(1) Normalized standard deviations of wind speeds/ui * were plotted as a function only of a local stability parameter. The values under near-neutral conditions were 2.15, 1.61, and 1.19 at 47 m, 2.39, 1.75,and 1.21 at 140 m, and 2.51, 1.77, and 1.30 at 280 m, showing a clear increase with height. The normalized standard deviation of wind components fitted the 1/3 law under unstable stratification conditions and decreased with height under both stable and unstable conditions.(2) The normalized standard deviation of temperature fitted the.1/3 law in the free convection limit, but was quite scattered with different characteristics under near-neutral conditions. The normalized standard deviations of humidity and the CO2 concentration fitted the.1/3 law under unstable conditions, and remained constant under near-neutral and stable stratification. The normalized standard deviation of scalars, i.e., temperature, humidity, and CO2 concentration, all increased with height.(3) Compared with momentum, and the water vapor and CO2 concentrations, the turbulence correlation coefficient for heat was smaller under near-neutral conditions, but larger under both stable and unstable conditions. A dissimilarity between heat, and the water vapor and CO2 concentrations was observed in urban areas. The relative correlation coefficients between heat and each of momentum, humidity, and CO2 concentration(|rwT/ruw|, |rwT/rwc| and |rwT/ruq|) in the lower layers were always larger than in higher layers, except for the relative correlation coefficient between heat and humidity in an unstable stratification. Therefore, the ratio between heat and each of momentum, humidity, and CO2 concentration decreased with height.