Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE

Version 1 : Received: 6 June 2020 / Approved: 7 June 2020 / Online: 7 June 2020 (14:51:03 CEST)

How to cite: Nazir, A.; Younis, M.S.; Shahzad, M.K. MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE. Preprints 2020, 2020060090 (doi: 10.20944/preprints202006.0090.v1). Nazir, A.; Younis, M.S.; Shahzad, M.K. MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE. Preprints 2020, 2020060090 (doi: 10.20944/preprints202006.0090.v1).

Abstract

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

Subject Areas

Noise Removal; Image Enhancement; MFNR; multi-dimensional data

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