3D Reconstruction for motion blurred images using deep learning-based intelligent systems

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 BFWGAN 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.