Underwater processing and manufacturing plays an important role in aviation

shipbuilding and other fields. In situ online 3-D detection of manufacturing process has become an urgent demand for underwater manufacturing quality assurance. Fringe projection profilometry (FPP)

as one of the classic optical 3-D measurement technologies

holds the advantages of non-contact

high speed and high accuracy. However

in turbid water

due to the absorption and scattering of light

the fringe light intensity captured by the camera is attenuated

the contrast is reduced

the image details are blurred

and a lot of noise is introduced

resulting in poor fringe image quality. The phase calculated by the low-quality fringes has the non-negligible phase error

resulting in the decrease of the 3-D measurement accuracy. In order to reduce the influence of underwater absorption and scattering

an end-to-end fringe image enhancement algorithm based on deep learning is proposed. The fringe pattern enhancement convolutional neural network (FPENet) is used to convert the low contrast and high noise fringes into high contrast and low noise fringes to obtain more accurate phase results. FPENet can effectively improve fringe quality and reduce phase error for water with different turbidity. Especially in high turbidity water

the phase error can be reduced by about 50%

significantly improving the measurement accuracy of underwater FPP

which is of great significance for improving the applicability of FPP in complex scenes.