Joint Image Denoising and Disparity Estimation via Stereo Structure PCA and Noise-Tolerant Cost

Jianbo Jiao, Qingxiong Yang, Shengfeng He, Shuhang Gu, Lei Zhang, Rynson W.H. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

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 multiscale 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 multiscale 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.

Original languageEnglish
Pages (from-to)204-222
Number of pages19
JournalInternational Journal of Computer Vision
Volume124
Issue number2
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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