Enhancing the Quality of Fog/Mist Images by Comparing the Effectiveness of Kalman Filter and Adaptive Filter for Noise Reduction

T. Srinivasulu, J. Sheela

2023

Abstract

The primary objective of this study is to enhance the precision of fog and mist noise reduction in photographs by introducing a novel Kalman filter and comparing its performance to that of an Adaptive filter. Materials and Methods: For this investigation, the research dataset was sourced from the Kaggle database system. Using twenty iteration samples (ten for Group 1 and ten for Group 2), involving a total of 1240 samples, the efficacy of fog and mist noise elimination with improved accuracy was assessed. This evaluation was conducted employing a G-power of 0.8, a 95% confidence interval, and alpha and beta values of 0.05 and 0.2, respectively. The determination of the sample size was based on the outcomes of these calculations. The novel Kalman filter and the Adaptive filter, both utilizing the same number of data samples (N=10), were employed for fog and mist noise removal from images. Notably, the Kalman filter exhibited a higher accuracy rate. Results: The novel Kalman filter showcased a success rate of 96.34%, outperforming the Adaptive filter's success rate of 93.78%. This difference in performance is statistically significant. The study's significance threshold was set at p=.001 (p<0.05), confirming the significance of the hypothesis. This analysis was carried out through an independent sample T-test. Conclusion: In conclusion, the proposed Kalman filter model, achieving an accuracy rate of 96.34%, demonstrates superior performance compared to the Adaptive filter, which yielded an accuracy rate of 93.78%. This comparison underscores the efficacy of the Kalman filter in the context of image noise removal.

Paper Citation

in Harvard Style

Srinivasulu T. and Sheela J. (2023). Enhancing the Quality of Fog/Mist Images by Comparing the Effectiveness of Kalman Filter and Adaptive Filter for Noise Reduction. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 5-12. DOI: 10.5220/0012572200003739

in Bibtex Style

@conference{ai4iot23,
author={T. Srinivasulu and J. Sheela},
title={Enhancing the Quality of Fog/Mist Images by Comparing the Effectiveness of Kalman Filter and Adaptive Filter for Noise Reduction},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012572200003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Enhancing the Quality of Fog/Mist Images by Comparing the Effectiveness of Kalman Filter and Adaptive Filter for Noise Reduction
SN - 978-989-758-661-3
AU - Srinivasulu T.
AU - Sheela J.
PY - 2023
SP - 5
EP - 12
DO - 10.5220/0012572200003739
PB - SciTePress