A DEEP LEARNING FRAMEWORK FOR 3D SURFACE PROFILING OF THE OBJECTS USING DIGITAL HOLOGRAPHIC INTERFEROMETRY

Phase reconstruction in Digital Holographic Interferometry (DHI) is widely employed for 3D deformation measurements of the object surfaces. The key challenge in phase reconstruction in DHI is in the estimation of the absolute phase from noisy reconstructed interference fringes. In this paper, we propose a novel efficient deep learning approach for the phase estimation from noisy interference fringes in DHI. The proposed approach takes noisy reconstructed interference fringes as input and estimates the 3D deformation field or the object surface profile as the output. The 3D deformation field measurement of the object is posed as the absolute phase estimation from the noisy wrapped phase, that can be obtained from the reconstructed interference fringes through arctan function. The proposed deep neural network is trained to predict the fringe-order through a fully convolutional semantic segmentation network, from the noisy wrapped phase. These predictions are improved by simultaneously minimizing the regression error between the true phase corresponding to the object deformation field and the estimated absolute phase considering the predicted fringe order. We compare our method with conventional methods as well as with the recent state-of-the-art deep learning phase unwrapping methods. The proposed method outperforms conventional approaches by a large margin, while we can observe significant improvement even with respect to recently proposed deep learning-based phase unwrapping methods, in the presence of noise as high as 0 dB to -5 dB.