NDLI: Denoising hyperspectral images using spectral domain statistics

Content Provider	IEEE Xplore Digital Library
Author	Lam, A. Sato, I. Sato, Y.
Copyright Year	2012
Description	Author affiliation: Digital Content and Media Sciences Research Division, National Institute of Informatics (Lam, A.; Sato, I.) \|\| Institute of Industrial Science, The University of Tokyo (Sato, Y.)
Abstract	Hyperspectral imaging has proven useful in a diverse range of applications in agriculture, diagnostic medicine, and surveillance to name a few. However, conventional hyperspectral images (HSIs) tend to be noisy due to limited light in individual bands; thus making denoising necessary. Previous methods for HSI de-noising have viewed the entire HSI as a general 3D volume or focused on processing the spatial domain. However, past findings suggest that spectral distributions exhibit less variation than spatial patterns. Hence it would be fruitful to take specific advantage of the more predictable behavior of spectral domain data for denoising. In this paper, we present a two-stage de-noising framework that first emphasizes denoising in the spectral domain and then uses spatial information to further improve spectral domain denoising. Our results indicate that specifically leveraging the spectral domain for denoising can provide state-of-the-art performance even from a relatively simple approach.
Starting Page	477
Ending Page	480
File Size	811631
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Spectral analysis Noise reduction Principal component analysis Noise Noise measurement Vectors Hyperspectral imaging
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Color-line vector field and local color component decomposition for smoothing and denoising of color images

Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model

Multiscale windowed denoising and segmentation of hyperspectral images

Denoising of hyperspectral imagery using a spatial-spectral domain mixing prior

Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising

Wavelet Denoising of Multicomponent Images, using a Noise-Free Image

CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images

Classification improvement based on non-white noise reduction using parafac decompositon for hyperspectral images

Mixed Gaussian and impulse denoising of hyperspectral images

Denoising hyperspectral images using spectral domain statistics

Similar Documents

Color-line vector field and local color component decomposition for smoothing and denoising of color images

Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model

Multiscale windowed denoising and segmentation of hyperspectral images

Denoising of hyperspectral imagery using a spatial-spectral domain mixing prior

Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising

Wavelet Denoising of Multicomponent Images, using a Noise-Free Image

CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images

Classification improvement based on non-white noise reduction using parafac decompositon for hyperspectral images

Mixed Gaussian and impulse denoising of hyperspectral images

Denoising hyperspectral images using spectral domain statistics