Document Type
Article
Publication Date
7-2016
Publication Source
Proceedings of the 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS)
Abstract
In this paper, we describe a new recursive Non-Local means (RNLM) algorithm for video denoising that has been developed by the current authors. Furthermore, we extend this work by incorporating a Poisson-Gaussian noise model. Our new RNLM method provides a computationally efficient means for video denoising, and yields improved performance compared with the single frame NLM and BM3D benchmarks methods. Non-Local means (NLM) based methods of denoising have been applied successfully in various image and video sequence denoising applications. However, direct extension of this method from 2D to 3D for video processing can be computationally demanding. The RNLM approach takes advantage of recursion for computational savings, and spatio-temporal correlations for improved performance. In our approach, the first frame is processed with single frame NLM. Subsequent frames are estimated using a weighted combination of the current frame NLM, and the previous frame estimate. Block matching registration with the prior estimate is done for each current pixel estimate to maximize the temporal correlation. To address the Poisson-Gaussian noise model, we make use of the Anscombe transformation prior to filtering to stabilize the noise variance. Experimental results are presented that demonstrate the effectiveness of our proposed method. We show that the new method outperforms single frame NLM and BM3D.
Inclusive pages
318-322
ISBN/ISSN
2379-2027
Copyright
Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher
IEEE
Keywords
Noise reduction, Tuning, Correlation, Digital images, Additives, Video sequences, Gaussian noise
eCommons Citation
Almahdi, Redha A. and Hardie, Russell C., "Recursive Non-Local Means Filter for Video Denoising with Poisson-Gaussian Noise" (2016). Electrical and Computer Engineering Faculty Publications. 408.
https://ecommons.udayton.edu/ece_fac_pub/408
Included in
Computer Engineering Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons
Comments
The document available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file.
The paper was later published in the EURASIP Journal on Image and Video.
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.