High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Thomas Müller

doi:10.1117/12.2224517

17 May 2016 High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Thomas Müller

Author Affiliations +

Proceedings Volume 9828, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII; 98280J (2016) https://doi.org/10.1117/12.2224517
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

The necessity to process interlaced images in surveillance, reconnaissance, or further computer vision areas should be a topic of the past. But, for different reasons, it is not. So, there are situations in practice, in which interlaced images have to be processed. Since a lot of algorithms strongly degrade when working with such images directly, a usual method is to double or interpolate image lines in order to discard one of the two enclosed image frames. This is efficient but leads to weak results, in which half of the original information is lost. Alternatively, a lot of valuable computation time has to be spent to solve the highly complex motion compensation task in order to improve the results significantly. In this paper, an efficient algorithm is presented to solve this dilemma. First, the algorithm solves the complex 2-D mapping problem using the best state-of-theart optical flow method that could be found for this purpose. But, of course, for different physical reasons there are regions which cannot properly be handled by optical flow by itself. Therefore, an efficient post-processing method detects and removes remaining artifacts afterwards, which is the main contribution of this paper. This method is based on color interpolation incorporating local image structure. The presented results document the overall performance of the approach with respect to obtained image quality and calculation time. The method is easy to implement and offers a valuable pre-processing for a lot of computer vision tasks.

Citation Download Citation

Thomas Müller "High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks", Proc. SPIE 9828, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII, 98280J (17 May 2016); https://doi.org/10.1117/12.2224517

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
15 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Optical flow

Image processing

Motion estimation

Video

Cameras

Video surveillance

Surveillance

Show All Keywords

Keywords/Phrases

Search In:

Publication Years