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. Preprints2020, 2020060090. https://doi.org/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. https://doi.org/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. Preprints2020, 2020060090. https://doi.org/10.20944/preprints202006.0090.v1
APA Style
Nazir, A., Younis, M.S., & Shahzad, M.K. (2020). MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE. Preprints. https://doi.org/10.20944/preprints202006.0090.v1
Chicago/Turabian Style
Nazir, A., Muhammad Shahzad Younis and Muhammad Khurram Shahzad. 2020 "MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE" Preprints. https://doi.org/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.
Keywords
Noise Removal; Image Enhancement; MFNR; multi-dimensional data
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
