Publication Type : Conference Proceedings
Publisher : Procedia Computer Science, 7th International Conference on Advances in Computing and Communications , ICACC-2017, Elsevier,
Source : Procedia Computer Science, 7th International Conference on Advances in Computing and Communications , ICACC-2017, Elsevier, Volume 115, Rajagiri School of Engineering & Technology, Kochi, Kerala, India, p.399-406 (2017)
Keywords : Denoising, Hyperspectral image, Least square weighted regularization, Pre-processing technique, SNR, Spectral domain, Total variation
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : Yes
Year : 2017
Abstract : Denoising is one of the fundamental pre-processing tasks in image processing that improves the quality of the information in the image. Processing of hyperspectral images requires high computational power and time. In this paper, a denoising technique based on least square weighted regularization in the spectral domain is proposed. The proposed technique is experimented on standard hyperspectral datasets and also, the performance of the proposed least square denoising in spectral domain is compared with least square weighted regularization in the spatial domain and total variation based denoising method. The obtained results in terms of computational time, Signal-to-Noise Ratio calculations and visual interpretation depicts that the proposed technique performs comparably better than the existing methods such as least square and total variation based hyperspectral image denoising. © 2017 The Author(s).
Cite this Research Publication : Chippy Jayaprakash, Naveen Varghese Jacob, Renu .R.K., Sowmya, and Dr. Soman K. P., “Least Square Denoising in Spectral Domain for Hyperspectral Images”, Procedia Computer Science, 7th International Conference on Advances in Computing and Communications , ICACC-2017, vol. 115. Elsevier, Rajagiri School of Engineering & Technology, Kochi, Kerala, India, pp. 399-406, 2017.