Laser stripe image denoising using convolutional autoencoder

•Creating a dataset containing 520 labeled laser stripe images.•Pretrained convolutional autoencoders perform well in natural laser stripe image denoising.•Denoised laser stripe images significantly improve the accuracy of the structured light systems. Convolutional autoencoders are making a signifi...

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Bibliographic Details
Published in:Results in physics Vol. 11; pp. 96 - 104
Main Authors: Fang, Zhuoqun, Jia, Tong, Chen, Qiusheng, Xu, Ming, Yuan, Xi, Wu, Chengdong
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2018
Elsevier
Subjects:
Convolutional autoencoder
Deep learning
Depth perception
Image denoising
Laser stripe
Structured light
Deep learning
Depth perception
Structured light
Convolutional autoencoder
Laser stripe
Image denoising
ISSN:2211-3797, 2211-3797
Online Access:Get full text
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Summary:•Creating a dataset containing 520 labeled laser stripe images.•Pretrained convolutional autoencoders perform well in natural laser stripe image denoising.•Denoised laser stripe images significantly improve the accuracy of the structured light systems. Convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system.
ISSN:2211-3797
2211-3797
DOI:10.1016/j.rinp.2018.08.023