Authors:
Yuquan Xu
1
;
Seiichi Mita
1
;
Hossein Tehrani
2
and
Kazuhisa Ishimaru
3
Affiliations:
1
Toyota Technological Institute, Japan
;
2
DENSO Corporation, Japan
;
3
Nippon Soken Inc., Japan
Keyword(s):
Stereo Vision, Image Deblurring, Optical Flow, Cepstrum.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Stereo Vision and Structure from Motion
Abstract:
Although stereo vision has made great progress in recent years, there are limited works which estimate the
disparity for challenging scenes such as tunnel scenes. In such scenes, owing to the low light conditions and
fast camera movement, the images are severely degraded by motion blur. These degraded images limit the
performance of the standard stereo vision algorithms. To address this issue, in this paper, we combine the
stereo vision with the image deblurring algorithms to improve the disparity result. The proposed algorithm
consists of three phases: the PSF estimation phase; the image restoration phase; and the stereo vision phase.
In the PSF estimation phase, we introduce three methods to estimate the blur kernel, which are optical flow
based algorithm, cepstrum base algorithm and simple constant kernel algorithm, respectively. In the image
restoration phase, we propose a fast non-blind image deblurring algorithm to recover the latent image. In the
last phase, we propose a multi
-scale multi-path Viterbi algorithm to compute the disparity given the deblurred
images. The advantages of the proposed algorithm are demonstrated by the experiments with data sequences
acquired in the tunnel.
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