14 May 2017 Joint technique of fine object boundary recovery and foreground image deblur for video including moving objects
Author Affiliations +
Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380R (2017) https://doi.org/10.1117/12.2266746
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan
Abstract
Capturing large-scale outdoor scene by video camera becomes common for various purposes, such as city modeling, surveillance, etc., and demand of recovering high quality image from video data is increasing. Because outdoor scene includes several barriers with multiple depths and motions, e.g.., cars or fences, simply applying motion deblur technique to each frame makes some noise. Furthermore, since color is mixed with foreground and background object near occluding boundary, color separation method during deblurring process is needed to restore the objects. In this paper, we propose a method to recover original boundary of foreground object from multiple blurred input images of video data. By using the refined object boundary, artifact around the border is reduced and accurate deblurring in the whole image is performed. Since both techniques are based on statistical method, quality of recovered image becomes better, if a number of input image increases. Experimental results are shown to prove that our method successfully recovers the deblurred image even if there are severe motion blur and color mixture near occluding boundary.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuki Matsushita, Yuki Matsushita, Hiroshi Kawasaki, Hiroshi Kawasaki, Teruhisa Takano, Teruhisa Takano, Shintaro Ono, Shintaro Ono, Katsushi Ikeuchi, Katsushi Ikeuchi, } "Joint technique of fine object boundary recovery and foreground image deblur for video including moving objects", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380R (14 May 2017); doi: 10.1117/12.2266746; https://doi.org/10.1117/12.2266746
PROCEEDINGS
8 PAGES


SHARE
Back to Top