Back to articles
Articles
Volume: 31 | Article ID: art00004
Image
A snowfall noise elimination using moving object compositing method adaptable to natural boundary
  DOI :  10.2352/ISSN.2470-1173.2019.11.IPAS-251  Published OnlineJanuary 2019
Abstract

In recent years, the use of surveillance cameras is increasingly recommended and they have been installed in many places. Snowy conditions at the time of an accident were associated with the problem that cars and accident circumstances become difficult to discern in images shot during snowfall. Previous techniques proposed methods for elimination of noise caused by snow using image shift or dedicated filters for the elimination of snowfall in video. However, these are associated with issues such as inability to cope with heavy snowfall or moving objects fading from view or being hard to discern. The present study proposes a method for snowfall noise elimination by extracting moving objects using the travel and the size of the moving object region between continuous frames, and compositing images while correcting for brightness in the background images. By distinguishing between falling snow and other moving objects, we can prevent objects other than snowfall becoming invisible. Using video of actual vehicles driving in snowy conditions for our experiments, we confirmed that snowfall noise can be eliminated without moving objects in the video becoming invisible. Furthermore, we confirmed that moving objects can be incorporated into the composited background images without any sense that they are out of place.

Subject Areas :
Views 46
Downloads 2
 articleview.views 46
 articleview.downloads 2
  Cite this article 

Yoshihiro Sato, Koya Kokubo, Yue Bao, "A snowfall noise elimination using moving object compositing method adaptable to natural boundaryin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVII,  2019,  pp 251-1 - 251-6,  https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-251

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2019
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA