Publication Type : Conference Paper
Publisher : Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018
Source : Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, Institute of Electrical and Electronics Engineers Inc., Adhiparasakthi Engineering CollegeMelmaruvathur, Chennai; India, p.58-63 (2018)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057739740&doi=10.1109%2fICCSP.2018.8524265&partnerID=40&md5=e1e609c6000af6c1fc38992cae355dee
ISBN : 9781538635216
Keywords : De-noising, Hyperspectral imaging, IBBC, Image denoising, Image Enhancement, Least Square, Least squares approximations, Signal to noise ratio, Spectral domains, Spectral resolution, Spectroscopy, SSIM, Strain measurement, Total variation
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2018
Abstract : Hyperspectral image HSI with high spectral resolution will be always degraded by the noise accumulation. Therefore, image denoising is a fundamental preprocessing technique which improves the precision of successive processes like image classification, unmixing etc. In this paper, we compare least square LS weighted regularization in spectral domain with spatial least square and total variation TV denoising techniques. These methods are experimented on real, and noise simulated hyperspectral image datasets. The contrast and edges of the image are well preserved in the spectral LS. The image contrast varies in spatial LS, and edge informations are lost in TV. The experimental results show that, the spectral LS is superior to other two techniques in terms of visual interpretation, Signal-to-Noise Ratio SNR and Structural Similarity SSIM Index. © 2018 IEEE.
Cite this Research Publication : N. V. Jacob, Sowmya, and Dr. Soman K. P., “A Comparative Analysis of Total Variation and Least Square Based Hyperspectral Image Denoising Methods”, in Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, Adhiparasakthi Engineering CollegeMelmaruvathur, Chennai; India, 2018, pp. 58-63.