Interferogram blind denoising using deep residual learning for phase-shifting interferometry

• Autorzy: Xu Xiaoqing , Xie Ming , Chen Song , Ji Ying , Wang Yawei

Identyfikatory

DOI

10.37190/oa220108

• Języki publikacji: EN

Abstrakty

EN

The interferogram containing the noises often affects the accuracy of phase retrieval, leading to the degradation of the phase imaging quality. To address this issue, a new interferogram blind denoising (IBD) method based on deep residual learning is proposed. In the presence of unknown noise levels, during the training, the deep residual convolutional neural networks (DRCNN) in the IBD approach is able to remove the latent clean interferogram implicitly, and then gradually establish the residual mapping relation in the pixel-level between the interferogram and the noises. With a well-trained DRCNN model, this algorithm can deal not only with the single-frame interferogram efficiently but also with the multi-frame phase-shifted interferograms collaboratively, while effectively retaining interferogram features related to phase retrieval. Simulation and experimental results demonstrate the feasibility and applicability of the proposed IBD method.

Słowa kluczowe

EN

interferogram denoising; deep learning; interferometry

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2022
• Tom: Vol. 52, nr 1
• Strony: 101--116
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Xu Xiaoqing

• Institute of Mold Technology, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, Jiangsu, China

autor

Xie Ming

• Faculty of Science, Jiangsu University, Zhenjiang 212013, China

autor

Chen Song

• Institute of Mold Technology, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, Jiangsu, China

autor

Ji Ying

• Faculty of Science, Jiangsu University, Zhenjiang 212013, China

autor

Wang Yawei

• Faculty of Science, Jiangsu University, Zhenjiang 212013, China

Bibliografia

• [1] YAQOOB Z., PSALTIS D., FELD M., YANG C., Optical phase conjugation for turbidity suppression in biological samples, Nature Photonics 2(2), 2008, pp. 110–115, DOI: 10.1038/nphoton.2007.297.
• [2] XU X., WANG Y., JI Y., XU Y., XIE M., HAN H., A novel dual-wavelength iterative method for generalized dual-wavelength phase-shifting interferometry with second-order harmonics, Optics and Lasers in Engineering 106, 2018, pp. 39–46, DOI: 10.1016/j.optlaseng.2018.02.007.
• [3] XU X., WANG Y., XU Y., JIN W., Simultaneous measurement of refractive index and thickness for optically transparent object with a dual-wavelength quantitative technique, Optica Applicata 46(4), 2016, pp. 597–605, DOI: 10.5277/oa160407.
• [4] XU X., WANG Y., XU Y., JIN W., Dual-wavelength in-line phase-shifting interferometry based on two dc-term-suppressed intensities with a special phase shift for quantitative phase extraction, Optics Letters 41(11), 2016, pp. 2430–2433, DOI: 10.1364/OL.41.002430.
• [5] VARGAS J., QUIROGA J.A., SORZANO C.O.S., ESTRADA J.C., CARAZO J.M., Two-step interferometry by a regularized optical flow algorithm, Optics Letters 36(17), 2011, pp. 3485–3487, DOI: 10.1364/OL.36.003485.
• [6] WANG H., KEMAO Q., GAO W., LIN F., SEAH H.S., Fringe pattern denoising using coherence-enhancing diffusion, Optics Letters 34(8), 2009, pp. 1141–1143, DOI: 10.1364/OL.34.001141.
• [7] QIAN KEMAO, Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications, and implementations, Optics and Lasers in Engineering 45(2), 2007, pp. 304–317, DOI: 10.1016/j.optlaseng.2005.10.012.
• [8] FU S., ZHANG C., Fringe pattern denoising via image decomposition, Optics Letters 37(3), 2012, pp. 422–424, DOI: 10.1364/OL.37.000422.
• [9] VARGAS J., SORZANO C.O.S., QUIROGA J.A., ESTRADA J.C., CARAZO J.M., Fringe pattern denoising by image dimensionality reduction, Optics and Lasers in Engineering 51(7), 2013, pp. 921–928, DOI: 10.1016/j.optlaseng.2013.02.016.
• [10] DABOV K., FOI A., KATKOVNIK V., EGIAZARIAN K., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing 16(8), 2007, pp. 2080–2095, DOI: 10.1109/TIP.2007.901238.
• [11] LECUN Y., BENGIO Y., HINTON G., Deep learning, Nature 521(7553), 2015, pp. 436–444, DOI: 10.1038/nature14539.
• [12] LECUN Y., BOTTOU L., BENGIO Y., HAFFNER P., Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11), 1998, pp. 2278–2324, DOI: 10.1109/5.726791.
• [13] KRIZHEVSKY A., SUTSKEVER I., HINTON G., ImageNet classification with deep convolutional neural networks, [In] International Conference on Neural Information Processing Systems, 2012, pp. 1097–1105.
• [14] SHELHAMER E., LONG J., DARRELL T., Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 2017, pp. 640–651, DOI: 10.1109/TPAMI.2016.2572683.
• [15] FALK T., MAI D., BENSCH R., CICEK O., ABDULKADIR A., MARRAKCHI Y., BÖHM A., DEUBNER J., JÄCKEL Z., SEIWALD K., DOVZHENKO A., TIETZ O., BOSCO C.D., WALSH S., SALTUKOGLU D., TAY T.L., PRINZ M., PALME K., SIMONS M., DIESTER I., BROX T., RONNEBERGER O., U-Net: deep learning for cell counting, detection, and morphometry, Nature Methods 16(1), 2019, pp. 67–70, DOI: 10.1038/s41592-018-0261-2.
• [16] WU Y., RIVENSON Y., ZHANG Y., WEI Z., GUNAYDIN H., LIN X., OZCAN A., Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery, Optica 5(6), 2018, pp. 704–710, DOI: 10.1364/OPTICA.5.000704.
• [17] XU X., XIE M., JI Y., WANG Y., Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning, Journal of the Optical Society of America A 38(3), 2021, pp. 321–327, DOI: 10.1364/JOSAA.412433.
• [18] NGUYEN T., BUI V., LAM V., RAUB C.B., CHANG L.-C., NEHMETALLAH G., Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection, Optics Express 25(13), 2017, pp. 15043–15057, DOI: 10.1364/OE.25.015043.
• [19] HELGADOTTIR S., ARGUN A., VOLPE G., Digital video microscopy enhanced by deep learning, Optica 6(4), 2019, pp. 506–513, DOI: 10.1364/OPTICA.6.000506.
• [20] HE K., ZHANG X., REN S., SUN J., Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778, DOI: 10.1109/CVPR.2016.90.
• [21] ZHANG K., ZUO W., CHEN Y., MENG D., ZHANG L., Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising, IEEE Transactions on Image Processing 26(7), 2016, pp. 3142–3155, DOI: 10.1109/TIP.2017.2662206.
• [22] IOFFE S., SZEGEDY C., Batch normalization: accelerating deep network training by reducing internal covariate shift, arXiv:1502.03167, 2015, DOI: 10.48550/arXiv.1502.03167.
• [23] NAIR V., HINTON G., Rectified linear units improve restricted Boltzmann machines, [In] Proceedings of the International Conference on Machine Learning (ICML-10), 2015, pp. 807–814.
• [24] WANG Z., HAN B., Advanced iterative algorithm for phase extraction of randomly phase-shifted interferograms, Optics Letters 29(14), 2004, pp. 1671–1673, DOI: 10.1364/OL.29.001671.
• [25] WANG Z., HAN B., Advanced iterative algorithm for randomly phase-shifted interferograms with intra- and inter- frame intensity variations, Optics and Lasers in Engineering 45(2), 2007, pp. 274–280, DOI: 10.1016/j.optlaseng.2005.11.003.
• [26] DENG J., WANG H., ZHANG F., ZHANG D., ZHONG L., LU X., Two-step phase demodulation algorithm based on the extreme value of interference, Optics Letters 37(22), 2012, pp. 4669–4671, DOI: 10.1364/OL.37.004669.