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Recursive Non-Local Means Filter for Video Denoising

Almahdi, Redha A.
http://orcid.org/0000-0002-0379-7313
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481033972368771

Abstract Details

2016, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
In this thesis, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on Non-Local Means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patched, within a given search window. The weights are based on the patch intensity vector dis- tances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally de- manding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here we propose a novel Recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for cop- mutationally savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current-frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality, as well as processing speed.
Russell Hardie, Ph.D (Advisor)
Vijayan Asari, Ph.D (Committee Member)
John Loomis, Ph.D (Committee Member)
39 p.
Computer Engineering; Electrical Engineering
Denoising; Video restoration; Non-Local Means; Recursive

Recommended Citations

Citations

  • Almahdi, R. A. (2016). Recursive Non-Local Means Filter for Video Denoising [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481033972368771

    APA Style (7th edition)

  • Almahdi, Redha. Recursive Non-Local Means Filter for Video Denoising. 2016. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481033972368771.

    MLA Style (8th edition)

  • Almahdi, Redha. "Recursive Non-Local Means Filter for Video Denoising." Master's thesis, University of Dayton, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481033972368771

    Chicago Manual of Style (17th edition)

dayton1481033972368771
735
© 2016, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.