  Optical fringe patterns are often contaminated by speckle noise, making it
difficult to accurately and robustly extract their phase fields. To deal with
this problem, we propose a filtering method based on deep learning, called
optical fringe patterns denoising convolutional neural network (FPD-CNN), for
directly removing speckle from the input noisy fringe patterns. Regularization
technology is integrated into the design of deep architecture. Specifically,
the FPD-CNN method is divided into multiple stages, each stage consists of a
set of convolutional layers along with batch normalization and leaky rectified
linear unit (Leaky ReLU) activation function. The end-to-end joint training is
carried out using the Euclidean loss. Extensive experiments on simulated and
experimental optical fringe patterns,especially finer ones with high-density
regions, show that the proposed method is competitive with some
state-of-the-art denoising techniques in spatial or transform domains,
efficiently preserving main features of fringe at a fairly fast speed.
