Joint image denoising and disparity estimation via stereo structure PCA and noise-tolerant cost

Publication Type

Journal Article

Publication Date

9-2017

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.

Keywords

Stereo matching, Image denoising, Disparity estimation, Non-local means

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

International Journal of Computer Vision

Volume

124

Issue

2

First Page

204

Last Page

222

ISSN

0920-5691

Identifier

10.1007/s11263-017-1015-9

Publisher

Springer

Additional URL

https://doi.org/10.1007/s11263-017-1015-9

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