Speckle Noise Reduction of Holograms Based on Spectral Convolutional Neural Network

Wenjing Zhou; Shuai Zou; Dengke He; Jinglu Hu; Yingjie Yu

doi:10.3788/AOS202040.0509001

[1] Kim M K. Applications of digital holography in biomedical microscopy[J]. Journal of the Optical Society of Korea, 14, 77-89(2010).

[2] Colomb T, Jourdain P, Marquet P et al. Enhancing the performance of digital holographic microscopy[J]. Proceedings of SPIE(2007).

[3] Blanche P A, Bablumian A, Voorakaranam R et al. Holographic three-dimensional telepresence using large-area photorefractive polymer[J]. Nature, 468, 80-83(2010).

[4] Huang L L, Chen X Z, Mühlenbernd H et al. Three-dimensional optical holography using a plasmonic metasurface[J]. Nature Communications, 4, 2808(2013).

[5] Lucente M. Interactive three-dimensional holographic displays: seeing the future in depth[J]. ACM SIGGRAPH Computer Graphics, 31, 63-67(1997).

[6] Hesselink L, Orlov S S, Bashaw M C. Holographic data storage systems[J]. Proceedings of the IEEE, 92, 1231-1280(2004).

[7] Hesselink L, Orlov S S, Liu A et al. Photorefractive materials for nonvolatile volume holographic data storage[J]. Science, 282, 1089-1094(1998).

[8] Burr G W, Ashley J, Coufal H et al. Modulation coding for pixel-matched holographic data storage[J]. Optics Letters, 22, 639-641(1997).

[9] Hariharan P. Optical holography:principles, techniques, and applications[M]. // Hariharan P,Knight P L, Miller A. Optical Holography: Principles, Techniques, and Applications.(1984).

[10] Herrera-Ramirez J. Hincapie-Zuluaga D A, Garcia-Sucerquia J. Speckle noise reduction in digital holography by slightly rotating the object[J]. Optical Engineering, 55, 121714(2016).

[11] Kang X. An effective method for reducing speckle noise in digital holography[J]. Chinese Optics Letters, 6, 100-103(2008). http://www.opticsjournal.net/Articles/Abstract?aid=OJ080305000134pWsYv2

[12] Quan C, Kang X, Tay C J. Speckle noise reduction in digital holography by multiple holograms[J]. Optical Engineering, 46, 115801(2007).

[13] Veronesi W A, Maynard J D. Digital holographic reconstruction of sources with arbitrarily shaped surfaces[J]. The Journal of the Acoustical Society of America, 85, 588-598(1989).

[14] Tu Q, Yu Y J, Zhou W J. Holographic system denoising based on rotary ground glass[J]. Optical Instruments, 36, 337-341(2014).

[15] Gong G H, Zhang H M, Yao M Y. Speckle noise reduction algorithm with total variation regularization in optical coherence tomography[J]. Optics Express, 23, 24699-24712(2015).

[16] Bianco V, Paturzo M, Memmolo P et al. Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography[J]. Optics Letters, 38, 619-621(2013).

[17] Fukuoka T, Mori Y, Nomura T. Speckle reduction by spatial-domain mask in digital holography[J]. Journal of Display Technology, 12, 315-322(2016).

[18] Kostadin D, Alessandro F, Vladimir K et al. Image restoration by sparse 3D transform-domain collaborative filtering[J]. Proceedings of SPIE, 6812, 681207(2008).

[19] Qian K M, Wang H X, Gao W J et al. Phase extraction from arbitrary phase-shifted fringe patterns with noise suppression[J]. Optics and Lasers in Engineering, 48, 684-689(2010).

[20] Yao D, Zheng K Y, Liu Z D et al. Wavelet denoising in near-infrared broadband cavity-enhanced absorption spectroscopy[J]. Acta Optica Sinica, 39, 0930006(2019).

[21] Cheng Z, He F, Zhang S L et al. Combination method of wavelet and empirical mode decomposition with trend modulation used for atmospheric coherent length profile denoising[J]. Acta Optica Sinica, 37, 1201002(2017).

[22] Feng S, Chen Q, Gu G et al. Fringe pattern analysis using deep learning[J]. Advanced Photonics, 1, 025001(2019). http://www.opticsjournal.net/Articles/Abstract?aid=OJ1903140002050FcIeL

[23] Wang K Q, Li Y, Qian K M et al. One-step robust deep learning phase unwrapping[J]. Optics Express, 27, 15100-15115(2019).

[24] Wang H, Lyu M. Situ G H. eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction[J]. Optics Express, 26, 22603-22614(2018).

[25] Zhang K, Zuo W, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

[26] Xie J, Xu L, Chen E. Image denoising and inpainting with deep neural networks. [C]//Advances in Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, United States. Canada: NIPS, 341-349(2012).

[27] Jeon W, Jeong W, Son K et al. Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks[J]. Optics Letters, 43, 4240-4243(2018).

[28] Zhang K, Zuo W, Zhang L. FFDNet: toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 27, 4608-4622(2018).

[29] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. [C]//Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. [S.l.: s.n.], 807-814(2010).

[30] Ioffe S. -03-02)[2019-09-01][EB/OL]. Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift., top/abs/1502, 03167(2015). https://arxiv.xilesou.

[31] Anaya J, Barbu A. RENOIR-A dataset for real low-light image noise reduction[J]. Journal of Visual Communication and Image Representation, 51, 144-154(2018).

[32] Nam S, Hwang Y, Matsushita Y et al. A holistic approach to cross-channel image noise modeling and its application to image denoising. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1683-1691(2016).

[33] Plotz T, Roth S. Benchmarking denoising algorithms with real photographs. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2750-2759(2017).

[34] Xu J, Li H, Liang Z et al. -04-07)[2019-09-01], top/abs/1804, 02603(2018). https://arxiv.xilesou.

[35] Abdelhamed A, Lin S, Brown M S. A high-quality denoising dataset for smartphone cameras. [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 1692-1700(2018).

[36] Zhou W J, Guan X F, Liu F F et al. Phase retrieval based on transport of intensity and digital holography[J]. Applied Optics, 57, A229-A234(2018).