NDLI: Video denoising based on a bivariate cauchy distribution in 3-D complex wavelet domain

Content Provider	IEEE Xplore Digital Library
Author	Rabbani, H. Vafadust, M. Gazor, S.
Copyright Year	2007
Description	Author affiliation: Dept. of Bioelectric, Amirkabir Univ. of Technol., Tehran (Rabbani, H.; Vafadust, M.; Gazor, S.)
Abstract	This paper presents a new video denoising algorithm based on the modeling of wavelet coefficients in each subband with a bivariate Cauchy probability density function (pdf). This bivariate pdf takes into account the statistical dependency of wavelet coefficients in adjacent scales. Within this framework, we describe a novel method for video denoising based on designing a maximum a posteriori (MAP) estimator employing a bivariate Cauchy random variable. Because separate 3-D transforms, such as ordinary 3-D wavelet transforms, have visual artifacts that degrade their performance in applications, we implement our algorithm in 3-D complex wavelet transform. This non-separable and oriented transform provides a motion-based multiscale decomposition for video that separates in its subbands motion along different directions. In addition, we use our denoising algorithm in 2-D complex wavelet domain, where the 2-D transform is applied to each frame individually. Despite the simplicity of our method in its implementation, our denoising results achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).
Starting Page	1
Ending Page	4
File Size	962644
Page Count	4
File Format	PDF
ISBN	9781424407781
DOI	10.1109/ISSPA.2007.4555435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-12
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Wavelet domain Discrete wavelet transforms PSNR Wavelet coefficients Discrete transforms Wavelet transforms Spatial resolution Image denoising Video sequences
Content Type	Text
Resource Type	Article

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

Image Denoising Employing a Bivariate Cauchy Distribution with Local Variance in Complex Wavelet Domain

Image denoising based on a mixture of bivariate gaussian distributions with local parameters in complex wavelet domain

salienShrink: Saliency-Based Wavelet Shrinkage

Using bivariate Gaussian distribution for image denoising in the 2-D complex wavelet domain

Video noise reduction in the wavelet domain using temporal decorrelation and adaptive thresholding

Locally adaptive multiscale Bayesian method for image denoising based on bivariate normal inverse Gaussian distributions

Image Denoising Based on Wavelet Edge Detection by Scale Multiplication

Image Denoising Based on A Mixture of Bivariate Gaussian Models in Complex Wavelet Domain

Image Denoising Based on A Mixture of Bivariate Laplacian Models in Complex Wavelet Domain

Video denoising based on a bivariate cauchy distribution in 3-D complex wavelet domain

Similar Documents

Image Denoising Employing a Bivariate Cauchy Distribution with Local Variance in Complex Wavelet Domain

Image denoising based on a mixture of bivariate gaussian distributions with local parameters in complex wavelet domain

salienShrink: Saliency-Based Wavelet Shrinkage

Using bivariate Gaussian distribution for image denoising in the 2-D complex wavelet domain

Video noise reduction in the wavelet domain using temporal decorrelation and adaptive thresholding

Locally adaptive multiscale Bayesian method for image denoising based on bivariate normal inverse Gaussian distributions

Image Denoising Based on Wavelet Edge Detection by Scale Multiplication

Image Denoising Based on A Mixture of Bivariate Gaussian Models in Complex Wavelet Domain

Image Denoising Based on A Mixture of Bivariate Laplacian Models in Complex Wavelet Domain

Video denoising based on a bivariate cauchy distribution in 3-D complex wavelet domain