Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75992
Title: Joint image denoising and disparity estimation via stereo structure PCA and noise-tolerant cost
Authors: Jiao, JB
Yang, QX
He, SF
Gu, SH 
Zhang, L 
Lau, RWH
Keywords: Stereo matching
Image denoising
Disparity estimation
Non-local means
Issue Date: 2017
Publisher: Springer
Source: International journal of computer vision, 2017, v. 124, no. 2, p. 204-222 How to cite?
Journal: International journal of computer vision 
Abstract: Stereo cameras are now commonly available on cars and mobile phones. However, the captured images may suffer from low image quality under noisy conditions, producing inaccurate disparity. In this paper, we aim at jointly restoring a clean image pair and estimating the corresponding disparity. To this end, we propose a new joint framework that iteratively optimizes these two different tasks in a multi-scale fashion. First, structure information between the stereo pair is utilized to denoise the images using a non-local means strategy. Second, a new noise-tolerant cost function is proposed for noisy stereo matching. These two terms are integrated into a multi-scale framework in which cross-scale information is leveraged to further improve both denoising and stereo matching. Extensive experiments on datasets captured from indoor, outdoor, and low-light conditions show that the proposed method achieves superior performance than the state-of-the-art image denoising and disparity estimation methods. While it outperforms multi-image denoising methods by about 2 dB on average, it achieves a 50% error reduction over radiometric-change-robust stereo matching on the challenging KITTI dataset.
URI: http://hdl.handle.net/10397/75992
ISSN: 0920-5691
EISSN: 1573-1405
DOI: 10.1007/s11263-017-1015-9
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