Deep Learning-Based Wrapped Phase Denoising Method for Application in Digital Holographic Speckle Pattern Interferometry

Abstract

This paper presents a new processing method for denoising interferograms obtained by digital holographic speckle pattern interferometry (DHSPI) to serve in the structural diagnosis of artworks. DHSPI is a non-destructive and non-contact imaging method that has been successfully applied to the structural diagnosis of artworks by detecting hidden subsurface defects and quantifying the deformation directly from the surface illuminated by coherent light. The spatial information of structural defects is mostly delivered as local distortions interrupting the smooth distribution of intensity during the phase-shifted formation of fringe patterns. Distortions in fringe patterns are recorded and observed from the estimated wrapped phase map, but the inevitable electronic speckle noise directly affects the quality of the image and consequently the assessment of defects. An effective method for denoising DHSPI wrapped phase based on deep learning is presented in this paper. Although a related method applied to interferometry for reducing Gaussian noise has been introduced, it is not suitable for application in DHSPI to reduce speckle noise. Thus, the paper proposes a new method to remove speckle noise in the wrapped phase. Simulated data and experimental captured data from samples prove that the proposed method can effectively reduce the speckle noise of the DHSPI wrapped phase to extract the desired information. The proposed method is helpful for accurately detecting defects in complex defect topography maps and may help to accelerate defect detection and characterization procedures.

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

“…Another difference is the fact that the noise added to the network during learning is usually not Gaussian white noise but pepper and salt noise or the Dropout of some of the input nodes. For stacked denoising autoencoder [21,22], which is commonly used in deep learning, the noise will be added to the output node of the previously learned layer, which is different from the scheme that only adds noise to the initial input and then learns all parameters at the same time in denoising applications. In fact, the most effective neural network in the field of denoising is the most basic multilayer perceptron model.…”

GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning

“…e proposed algorithm recovers high-quality fringe patterns compared with other denosing algorithms. In [26], the authors presented a deep learning-based method for denoising digital holographic speckle pattern interferometer (DHSPI) wrapped phase. e method proposed is very effective to extract the required information and reduce speckle noise.…”

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

“…For that, the captured noisy microinterferograms should be denoising before any analysing (Bianco et al, 2018; Paez & Strojnik, 1999; Rosendahl et al, 2010; Servin et al, 2009). The key to a successful denoising process is detected the class of noise in the recorded microinterferograms and hence using the appropriate filter to remove the detected noise (Rosendahl et al, 2010; Yan, Chang, Andrianakis, Tornari, & Yu, 2020; Yan, Yu, Sun, Sundi, & Kemao, 2020) Generally, there are various classes of noise such as periodic noise, salt and pepper noise, Poisson noise, speckle noise, Rayleigh noise, Gaussian noise, and others (Boyat & Joshi, 2015). However, the most classes that occur in microinterferogram patterns are speckle noise, salt and pepper noise, and Gaussian noise (Servin et al, 2009).…”

“…Various studies suggested many denoising methods for cleaning the noisy microinterferograms (Bianco et al, 2018; Paez & Strojnik, 1999; Rosendahl et al, 2010; Servin et al, 2009); Yan, Chang, et al, 2020; Yan, Yu, et al, 2020; Yan et al, 2019). Unfortunately, the majority of them relied on expert (without pre‐classifying) to detect the noise class which leads to increase the error rate in the demodulated phase objects from these noisy microinterferograms.…”