Vol.66, No.2, 2021, pp.2087-2104, doi:10.32604/cmc.2020.014220
3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems
  • Jing Zhang1,2, Keping Yu3,*, Zheng Wen4, Xin Qi3, Anup Kumar Paul5
1 Department of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, China
2 Tamoritsusho Co., Ltd., Tokyo, 110-0005, Japan
3 Global Information and Telecommunication Institute, Waseda University, Tokyo, 169-8050, Japan
4 School of Fundamental Science and Engineering, Waseda University, Tokyo, 169-8050, Japan
5 Department of Electronics and Communications Engineering, East West University, Dhaka, 1212, Bangladesh
* Corresponding Author: Keping Yu. Email:
(This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
Received 07 September 2020; Accepted 11 October 2020; Issue published 26 November 2020
The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image. Then, the blurred image and the corresponding sharp image are input into the WGAN. This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions. Next, we use the deblurred images generated by the BF-WGAN algorithm for 3D reconstruction. We propose a threshold optimization random sample consensus (TO-RANSAC) algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately. Compared with the traditional RANSAC algorithm, the TO-RANSAC algorithm can adjust the threshold adaptively, which improves the accuracy of the 3D reconstruction results. The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms. In addition, the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.
3D reconstruction; motion blurring; deep learning; intelligent systems; bilateral filtering; random sample consensus
Cite This Article
J. Zhang, K. Yu, Z. Wen, X. Qi and A. K. Paul, "3d reconstruction for motion blurred images using deep learning-based intelligent systems," Computers, Materials & Continua, vol. 66, no.2, pp. 2087–2104, 2021.
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