Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer

  • Sungjun Lim
  • Jong Chul YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)


Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored as a fast and high performance alternative. In this paper, we present a novel unsupervised deep neural network for blind deconvolution based on cycle consistency and PSF modeling layers. In contrast to the recent CNN approaches for similar problem, the explicit PSF modeling layers improve the robustness of the algorithm. Experimental results confirm the efficacy of the algorithm.


Microscopy Image reconstruction Machine learning 


  1. 1.
    Chan, T.F., Wong, C.K.: Total variation blind deconvolution. IEEE Trans. Image Process. 7(3), 370–375 (1998)CrossRefGoogle Scholar
  2. 2.
    Chaudhuri, S., Velmurugan, R., Rameshan, R.: Blind deconvolution methods: a review. In: Blind Image Deconvolution: Methods and Convergence, pp. 37–60. Springer, Cham (2014). Scholar
  3. 3.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). Scholar
  4. 4.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  5. 5.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)Google Scholar
  6. 6.
    Kang, E., Koo, H.J., Yang, D.H., Seo, J.B., Ye, J.C.: Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med. Phys. 46(2), 550–562 (2019)CrossRefGoogle Scholar
  7. 7.
    Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN: blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8183–8192 (2018)Google Scholar
  8. 8.
    Lu, Y., Tai, Y.W., Tang, C.K.: Conditional CycleGAN for attribute guided face image generation. arXiv preprint: arXiv:1705.09966 (2017)
  9. 9.
    Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)Google Scholar
  10. 10.
    Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)Google Scholar
  11. 11.
    McCann, M.T., Jin, K.H., Unser, M.: Convolutional neural networks for inverse problems in imaging: a review. IEEE Signal Process. Mag. 34(6), 85–95 (2017)CrossRefGoogle Scholar
  12. 12.
    McNally, J.G., Karpova, T., Cooper, J., Conchello, J.A.: Three-dimensional imaging by deconvolution microscopy. Methods 19(3), 373–385 (1999)CrossRefGoogle Scholar
  13. 13.
    Nehme, E., Weiss, L.E., Michaeli, T., Shechtman, Y.: Deep-storm: super-resolution single-molecule microscopy by deep learning. Optica 5(4), 458–464 (2018)CrossRefGoogle Scholar
  14. 14.
    Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  15. 15.
    Rivenson, Y., Göröcs, Z., Günaydin, H., Zhang, Y., Wang, H., Ozcan, A.: Deep learning microscopy. Optica 4(11), 1437–1443 (2017)CrossRefGoogle Scholar
  16. 16.
    Sarder, P., Nehorai, A.: Deconvolution methods for 3-D fluorescence microscopy images. IEEE Signal Process. Mag. 23(3), 32–45 (2006)CrossRefGoogle Scholar
  17. 17.
    Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint: arXiv:1607.08022 (2016)
  18. 18.
    Weigert, M., Royer, L., Jug, F., Myers, G.: Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017, Part II. LNCS, vol. 10434, pp. 126–134. Springer, Cham (2017). Scholar
  19. 19.
    You, Y.L., Kaveh, M.: A regularization approach to joint blur identification and image restoration. IEEE Trans. Image Process. 5(3), 416–428 (1996)CrossRefGoogle Scholar
  20. 20.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.KAIST Institute for Artificial IntelligenceDaejeonRepublic of Korea
  2. 2.Department of Bio/Brain EngineeringKAISTDaejeonRepublic of Korea

Personalised recommendations