Share Email Print
cover

Proceedings Paper

Image-registration-based local noise reduction for noisy video sequences
Author(s): Nan Jiang; Glen Abousleman; Jennie Si
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents a method for localizing noise-corrupted areas in quality degraded video frames, and for reducing the additive noise by utilizing the temporal redundancy in the video sequence. In the proposed algorithm, the local variance of each pixel is computed to obtain the spatial distribution of noise. After adaptive thresholding, region clustering, and merging, the corrupted areas of highest energy are detected. Due to the high temporal redundancy in the video sequence, the corrupted information can be compensated by overlapping the corrupted regions with the appropriate regions from adjacent video frames. The corresponding pixel locations in the adjacent frames are computed by using image registration and warping techniques. New pixel values are calculated based upon multi-frame stacking. Pixel values in the adjacent frames are weighted according to registration errors, whereas the values in the noisy frame are evaluated according to local variance. Knowing the location of the local noise enables the denoising process to be much more specific and accurate. Moreover, since only a portion of the frame is processed, as compared to standard denoising methods that operate on the entire frame, the details and features in other areas of the frame are preserved. The proposed scheme is applied to UAV video sequences, where the outstanding noise localization and reduction properties are demonstrated.

Paper Details

Date Published: 5 May 2006
PDF: 9 pages
Proc. SPIE 6209, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications III, 620902 (5 May 2006); doi: 10.1117/12.665122
Show Author Affiliations
Nan Jiang, Arizona State Univ. (United States)
Glen Abousleman, General Dynamics C4 Systems (United States)
Jennie Si, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 6209:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications III
Daniel J. Henry, Editor(s)

© SPIE. Terms of Use
Back to Top