KEYWORDS: Fringe analysis, Fourier transforms, Linear filtering, Neural networks, Phase shifts, Optical engineering, 3D modeling, Image denoising, 3D metrology, Video
Extracting phase from a single-frame fringe pattern is a key and challenging problem in fringe projection 3D measurement, especially for dynamic 3D measurement. We propose a single-shot phase extraction approach based on a low-pass filter with a well-trained image denoiser. Various comparative experiments have verified the effectiveness of the proposed method. More importantly, we associate the single-shot phase extraction problem with image denoising using deep learning. Thus more existing well-designed deep neural network models can be reused in the proposed method, without having to design a new model.
A number of studies demonstrate that illumination is an important factor impacting the performance of computer vision tasks and illumination normalization can improve the performance of other visual analysis algorithms. At present, there are few methods aiming to illumination normalization of color face with deep learning. For this reason, we put forward a novel and practical deep fully convolutional neural network architecture for illumination normalization of color face. Comparing with the current methods based on deep learning, our approach does not need to input identity and illumination label. We preserve the identity by a well-designed generator and content loss. Experimental results show that the proposed method achieves favorable illumination normalization effect under various lighting variances and preserves identity effectively.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.