English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In fringe projection profilometry, the high-order harmonics information ofnon-sinusoidal fringes will lead to errors in the phase estimation. In order tosolve this problem, a point-wise posterior phase estimation (PWPPE) methodbased on deep learning technique is proposed in this paper. The complexnonlinear mapping relationship between the multiple gray values and the sine /cosine value of the phase is constructed by using the feedforward neuralnetwork model. After the model training, it can estimate the phase values ofeach pixel location, and the accuracy is higher than the point-wiseleast-square (PWLS) method. To further verify the effectiveness of this method,a face mask is measured, the traditional PWLS method and the proposed PWPPEmethod are employed, respectively. The comparison results show that thetraditional method is with periodic phase errors, while the proposed PWPPEmethod can effectively eliminate such phase errors caused by non-sinusoidalfringes.

Point-wise posteriori phase estimation in high-precision fringe projection profilometry