ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
Minimum Noise Fraction with Multi-Directional Noise Estimation for denoising Hyperspectral Imagery
Siddharth Baudh1
Kamaldeep2
1 2Defence Geoinformatics Research Establishment, Chandigarh, India.
Published Online: September-December 2026
Pages: 182-187
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503023
References
1. Chien-I Chang and Qian Du, “Interference and noise-adjusted principal components analysis,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 37, no. 5, pp. 2387–2396, 1999. doi: 10.1109/36.789637.
2. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with
implications for noise removal,” IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65–74, 1988. doi:
10.1109/36.3001.
3. N. Renard, S. Bourennane, and J. Blanc-Talon, “Denoising and dimensionality reduction using multilinear tools for hyperspectral images,”
IEEE Geoscience and Remote Sensing Letters, 2008. doi: 10.1109/LGRS.2007.910416.
4. Chein-I Chang, Hyperspectral Data Exploitation: Theory and Applications, 2007.
5. Asgeir Bjorgan and Lise Lyngsnes Randeberg, “Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum
Noise Fraction-Based Approach,” Sensors, vol. 15, no. 2, pp. 3362–3378, 2015. doi: 10.3390/s150203362.
6. A. Bjorgan and L. L. Randeberg, “MNF-LBL Source Code,” 2015. https://github.com/ntnu-bioopt/mnf.
7. Wei Yang, Lianru Gao, and Dongmei Chen, “Real-time target detection in hyperspectral images based on spatial-spectral information
extraction,” EURASIP Journal on Advances in Signal Processing, vol. 2012, 2012. doi: 10.1186/1687-6180-2012-142.
8. Guolan Lu and Baowei Fei, “Medical hyperspectral imaging: a review,” Journal of Biomedical Optics, vol. 19, 2014. doi:
10.1117/1.JBO.19.1.010901.
9. Nasser M. Nasrabadi, “Hyperspectral Target Detection: An Overview of Current and Future Challenges,” IEEE Signal Processing Magazine,
vol. 31, no. 1, pp. 34–44, 2014. doi: 10.1109/MSP.2013.2278992.
10. Burak Uzkent, Aneesh Rangnekar, and Matthew J. Hoffman, “Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood
Maps,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 233–242, 2017. doi:
10.1109/CVPRW.2017.35.
11. T. F. Chan, G. H. Golub, and R. J. LeVeque, “Algorithms for computing the sample variance: Analysis and recommendations,” The American
Statistician, 1983. doi: 10.1080/00031305.1983.10483115.
12. Philippe P. Pebay, “Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments,” Sandia
National Laboratories, SAND2008-6212, 2008.
13. NVIDIA Corporation, “cuBLAS Library.” https://developer.nvidia.com/cublas.
Sensing, vol. 37, no. 5, pp. 2387–2396, 1999. doi: 10.1109/36.789637.
2. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with
implications for noise removal,” IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65–74, 1988. doi:
10.1109/36.3001.
3. N. Renard, S. Bourennane, and J. Blanc-Talon, “Denoising and dimensionality reduction using multilinear tools for hyperspectral images,”
IEEE Geoscience and Remote Sensing Letters, 2008. doi: 10.1109/LGRS.2007.910416.
4. Chein-I Chang, Hyperspectral Data Exploitation: Theory and Applications, 2007.
5. Asgeir Bjorgan and Lise Lyngsnes Randeberg, “Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum
Noise Fraction-Based Approach,” Sensors, vol. 15, no. 2, pp. 3362–3378, 2015. doi: 10.3390/s150203362.
6. A. Bjorgan and L. L. Randeberg, “MNF-LBL Source Code,” 2015. https://github.com/ntnu-bioopt/mnf.
7. Wei Yang, Lianru Gao, and Dongmei Chen, “Real-time target detection in hyperspectral images based on spatial-spectral information
extraction,” EURASIP Journal on Advances in Signal Processing, vol. 2012, 2012. doi: 10.1186/1687-6180-2012-142.
8. Guolan Lu and Baowei Fei, “Medical hyperspectral imaging: a review,” Journal of Biomedical Optics, vol. 19, 2014. doi:
10.1117/1.JBO.19.1.010901.
9. Nasser M. Nasrabadi, “Hyperspectral Target Detection: An Overview of Current and Future Challenges,” IEEE Signal Processing Magazine,
vol. 31, no. 1, pp. 34–44, 2014. doi: 10.1109/MSP.2013.2278992.
10. Burak Uzkent, Aneesh Rangnekar, and Matthew J. Hoffman, “Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood
Maps,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 233–242, 2017. doi:
10.1109/CVPRW.2017.35.
11. T. F. Chan, G. H. Golub, and R. J. LeVeque, “Algorithms for computing the sample variance: Analysis and recommendations,” The American
Statistician, 1983. doi: 10.1080/00031305.1983.10483115.
12. Philippe P. Pebay, “Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments,” Sandia
National Laboratories, SAND2008-6212, 2008.
13. NVIDIA Corporation, “cuBLAS Library.” https://developer.nvidia.com/cublas.
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