Laser stripe image denoising using convolutional autoencoder - Fang, Zhuoqun; Jia, Tong; Chen, Qiusheng; Xu, Ming; Yuan, Xi; Wu, Chengdong (Results in physics, 2018)
•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.