NDLI: Mixed Gaussian and impulse denoising of hyperspectral images

Content Provider	IEEE Xplore Digital Library
Author	Aggarwal, H.K. Majumdar, A.
Copyright Year	2015
Description	Author affiliation: Indraprastha Inst. of Inf. Technol., Delhi, India (Aggarwal, H.K.; Majumdar, A.)
Abstract	Hyperspectral image denoising is an important preprocessing step in the analysis of hyperspectral images in several applicaitons domains. These images often gets corrupted by various kinds of noise during acquisition process. There are several studies on reducing Gaussian noise from hyperspectral images. This work addresses the problem of reducing mixed noise from hyperspectral images; in particular a mixture of Gaussian and impulse noise has been considered. The proposed image acquisition model explicitly accounts for both Gaussian and impulse noise as additive noise. This mixed noise reduction problem has been formulated as synthesis prior optimization problem which exploits inherent spatio-spectral correlation present in hyperspectral images. Split-Bregman based approach has been utilized to solve resulting optimization problem. Experiements were conducted using both synthetic noise as well as real noisy hyperspectral images. Experimental results have been quantified using peak signal to noise ratio (PSNR) and structural similarity index (SSIM). A comparative study with an existing low-rank based image denoising approaches has also been carried out. Both quantitative and qualitative results suggest the superiority of proposed approach.
Starting Page	429
Ending Page	432
File Size	877128
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7325792
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Noise reduction Yttrium Gaussian noise Noise measurement Transforms Split-Bregman Impulse Noise Hyper-spectral Images
Content Type	Text
Resource Type	Article

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