Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Park, Chee Hyun; Chang, Joon‐Hyuk

doi:10.1155/2019/6592406

Wireless Communications and Mobile Computing

2019

DOI: 10.1155/2019/6592406

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Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Chee Hyun Park

Joon‐Hyuk Chang

Abstract: Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrated that the estimation accuracies of the proposed methods are higher than those of the existing median-based shrinkage methods through extensive simulations.

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Cited by 2 publications

References 24 publications

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A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization

Doumun OULAI,

Dabo‐Niang,

Zoueu

2024

Int J Imaging Syst Tech

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Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this normalization, aiming to improve the accuracy and reliability of the diagnostic process. This method is based on estimating the Bright reference image, which captures the luminosity, and the contrast variability function from the background region of the image. This is achieved through two distinct resampling methodologies, namely Gaussian random field simulation by variogram analysis and Bootstrap resampling. A method for handling the intensity saturation issue of certain pixels is also proposed, which involves outlier imputation. Both of these proposed approaches for image normalization are demonstrated to outperform existing methods for multispectral and multimodal unstained blood smear images, as measured by the Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Zero mean Sum of Absolute Differences (ZSAD), Peak Signal to Noise Ratio (PSNR), and Absolute Mean Brightness Error (AMBE). These methods not only improve the image contrast but also preserve its spectral footprint and natural appearance more accurately. The normalization technique employing Bootstrap resampling significantly reduces the acquisition time for multimodal and multispectral images by 66%. Moreover, the processing time for Bootstrap resampling is less than 4% of the processing time required for Gaussian random field simulation.

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A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization

Doumun OULAI,

Dabo‐Niang,

Zoueu

2024

Int J Imaging Syst Tech

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Robust range estimation algorithm based on hyper‐tangent loss function

Park

Chang

2020

IET signal process.

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Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper-tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.

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Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Cited by 2 publications

References 24 publications

A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization

A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization

Robust range estimation algorithm based on hyper‐tangent loss function

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