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| Author | van der Jeught, S.; Muyshondt, P.G.G.; Lobato, I. | ||||
| Title | Optimized loss function in deep learning profilometry for improved prediction performance | Type | A1 Journal article | ||
Year  |
2021 | Publication | JPhys Photonics | Abbreviated Journal | |
| Volume | 3 | Issue | 2 | Pages | 024014 |
| Keywords | A1 Journal article; Electron microscopy for materials research (EMAT) | ||||
| Abstract | Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Wos | 000641030000001 | Publication Date | 2021-03-18 | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2515-7647 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
| Impact Factor | Times cited | Open Access | OpenAccess | ||
| Notes | Approved | Most recent IF: NA | |||
| Call Number | UA @ admin @ c:irua:178171 | Serial | 6797 | ||
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