Fringe pattern analysis using deep learning

Abstract

In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.

High-speed in vitro intensity diffraction tomography

“…Recently, several groups have demonstrated multiple scattering models suitable for solving large-scale imaging problems [39,[54][55][56], which will be considered in our future work. Our model-based reconstruction approach is also constrained by unknown experimental variabilities that are difficult to be fully parameterized via an analytical model, which may be overcome using emerging learning-based inversion techniques [57][58][59][60][61][62][63][64].…”

Does deep learning always outperform simple linear regression in optical imaging?

“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement

“…The noise effect in the labeled data has never been paid attention. Also, in order to effectively train the DNN, scores of fringe pattern with labeled data should be prepared [20]. In this paper, we propose to extract the fringe part apart from background part as well as noise part with less samples in a new manner using deep learning as follows.…”

“…al. recently introduced the deep learning method into fringe pattern analysis and they proposed the deep neural network (DNN) to conduct phase retrieval from single frame fringe pattern [20,21]. The process of phase retrieval is learned from the input data and the output labeled data in the training dataset by DNN.…”