Deep Learning Based Image Restoration Method of Optical Synthetic Aperture Imaging System

Ju Tang; Kaiqiang Wang; Wei Zhang; Xiaoyan Wu; Guodong Liu; Jianglei Di; Jianlin Zhao

doi:10.3788/AOS202040.2111001

[1] Barakat R. Dilute aperture diffraction imagery and object reconstruction[J]. Optical Engineering, 29, 131-139(1990).

[2] Lü Y K, Wu Y H. Development and key technologies of synthetic aperture ladar imaging[J]. Laser & Optoelectronics Progress, 54, 100004(2017).

[3] Li D Y, Wu J, Wan L et al. Elementary imaging theory on space-borne synthetic aperture ladar[J]. Acta Optica Sinica, 39, 0728002(2019).

[4] Bell K D, Boucher R H, Vacek R et al. Assessment of large aperture lightweight imaging concepts[C]//1996 IEEE Aerospace Applications Conference. Proceedings, February 10, 1996, Aspen, CO, USA., 187-203(1996).

[5] Fienup J R, Griffith D K, Harrington L et al. Comparison of reconstruction algorithms for images from sparse-aperture systems[J]. Proceedings of SPIE, 4792, 1-8(2002).

[6] Wang D, Han J, Liu H et al. Experimental study on imaging and image restoration of optical sparse aperture systems[J]. Optical Engineering, 46, 103201(2007).

[7] Wu Q Y, Qian L, Shen W M. Image recovering for sparse-aperture systems[J]. Proceedings of SPIE, 5642, 478-486(2005).

[8] Liu L, Jiang Y S, Wang C W. Noise analysis and image restoration for optical sparse aperture systems[C]//2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote, 353-356(2008).

[9] Wei X F, Geng Z X, Cao L et al. A novel imaging performance index and pupil optimization method for optical synthetic aperture system[J]. Acta Optica Sinica, 34, 1111001(2014).

[10] Zhou C H, Wang Z L, Zhang S Q et al. Large aperture diffraction limited optical synthetic aperture system intermediate frequency MTF compensation[J]. Acta Optica Sinica, 38, 0411005(2018).

[11] Wang F, Jiang M Q, Qian C et al. Residual attention network for image classification[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6450-6458(2017).

[12] Sarikaya R, Hinton G E, Deoras A. Application of deep belief networks for natural language understanding[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22, 778-784(2014). http://dl.acm.org/citation.cfm?id=2687014

[13] Rivenson Y, Koydemir H C, Wang H et al. Deep learning enhanced mobile-phone microscopy[J]. ACS Photonics, 5, 2354-2364(2018).

[14] Lyu M, Wang H, Li G et al. Learning-based lensless imaging through optically thick scattering media[J]. Advanced Photonics, 1, 036002(2019). http://www.opticsjournal.net/Articles/Abstract?aid=OJcdeafc077f03aa8e

[15] Li Y Z, Xue Y J. -09-26)[2020-06-19][EB/OL]. Tian L. Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media., org/abs/1806, 04139(2018). https://arxiv.

[16] Zhang K, Zuo W M, 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).

[17] Yan K T, Yu Y J, Huang C T et al. Fringe pattern denoising based on deep learning[J]. Optics Communications, 437, 148-152(2019).

[18] Wang K Q, Dou J Z, Qian K M et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction[J]. Optics Letters, 44, 4765-4768(2019).

[19] Wang H, Lyu M, Chen N et al. -01-10)[2020-06-19](2018). https://www.researchgate.net/publication/325829075_In-line_hologram_reconstruction_with_deep_learning.

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

[21] Wang K Q, Di J L, Li Y et al. Transport of intensity equation from a single intensity image via deep learning[J]. Optics and Lasers in Engineering, 134, 106233(2020).

[22] Wei X F, Geng Z X, Song X. Detection and removal of ringing artifact for optical synthetic aperture restoration image[J]. Optics and Precision Engineering, 22, 3091-3099(2014).

[23] Xu W H, Zhao M, Li H S. Non-iterative wavelet-based deconvolution for sparse aperture system[J]. Optics Communications, 295, 36-44(2013).

[24] Kundur D, Hatzinakos D. Blind image deconvolution[J]. IEEE Signal Processing Magazine, 13, 43-64(1996).

[25] Kundur D, Hatzinakos D. A novel blind deconvolution scheme for image restoration using recursive filtering[J]. IEEE Transactions on Signal Processing, 46, 375-390(1998).

[26] Yang H L, Chiao Y H, Huang P H et al. Blind image deblurring with modified Richardson-Lucy deconvolution for ringing artifact suppression. [C]// Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology, November 20-23, 2011, Heidelberg: Springer, 240-251(2011).

[27] Ronneberger O, Fischer P, Brox T[M]. U-net: convolutional networks for biomedical image segmentation, 234-241(2015).

[28] Ioffe S. -02-11) [2020-06-19][EB/OL]. Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift., org/abs/1502, 03167(2015). https://arxiv.

[29] Srivastava N, Hinton G E, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014).

[30] HoréA, ZiouD. Image quality metrics: PSNR vs. SSIM[C]//2010 20th International Conference on Pattern Recognition, August 23-26, 2010, Istanbul, Turkey. New York: IEEE Press, 2010: 2366- 2369.

[31] Cheng G, Han J W, Lu X Q. Remote sensing image scene classification: benchmark and state of the art[J]. Proceedings of the IEEE, 105, 1865-1883(2017).