Texture preservation in de-noising UAV surveillance video through multi-frame sampling

Yi Wang; Ronald A. Fevig; Richard R. Schultz

doi:10.1117/12.806023

10 February 2009 Texture preservation in de-noising UAV surveillance video through multi-frame sampling

Yi Wang, Ronald A. Fevig, Richard R. Schultz

Proceedings Volume 7245, Image Processing: Algorithms and Systems VII; 72450A (2009) https://doi.org/10.1117/12.806023
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

Image de-noising is a widely-used technology in modern real-world surveillance systems. Methods can seldom do both de-noising and texture preservation very well without a direct knowledge of the noise model. Most of the neighborhood fusion-based de-noising methods tend to over-smooth the images, which causes a significant loss of detail. Recently, a new non-local means method has been developed, which is based on the similarities among the different pixels. This technique results in good preservation of the textures; however, it also causes some artifacts. In this paper, we utilize the scale-invariant feature transform (SIFT) [1] method to find the corresponding region between different images, and then reconstruct the de-noised images by a weighted sum of these corresponding regions. Both hard and soft criteria are chosen in order to minimize the artifacts. Experiments applied to real unmanned aerial vehicle thermal infrared surveillance video show that our method is superior to popular methods in the literature.

Citation Download Citation

Yi Wang, Ronald A. Fevig, and Richard R. Schultz "Texture preservation in de-noising UAV surveillance video through multi-frame sampling", Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 72450A (10 February 2009); https://doi.org/10.1117/12.806023

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available