Joint Image Denoising and Disparity Estimation via Stereo Structure PCA and Noise-Tolerant Cost
| العنوان: | Joint Image Denoising and Disparity Estimation via Stereo Structure PCA and Noise-Tolerant Cost |
|---|---|
| المؤلفون: | Rynson W. H. Lau, Lei Zhang, Shengfeng He, Jianbo Jiao, Qingxiong Yang, Shuhang Gu |
| المصدر: | International Journal of Computer Vision. 124:204-222 |
| بيانات النشر: | Springer Science and Business Media LLC, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Stereo cameras, business.industry, Image quality, Computer science, Noise reduction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, Non-local means, Artificial Intelligence, Computer Science::Computer Vision and Pattern Recognition, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, Noise (video), business, Joint (audio engineering), Software, Computer stereo vision |
| الوصف: | 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. |
| تدمد: | 1573-1405 0920-5691 |
| DOI: | 10.1007/s11263-017-1015-9 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::766220a42a09c8d943fde4bbef54a9e6 https://doi.org/10.1007/s11263-017-1015-9 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........766220a42a09c8d943fde4bbef54a9e6 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15731405 09205691 |
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| DOI: | 10.1007/s11263-017-1015-9 |