Morphological Networks for Image De-raining

  • Ranjan MondalEmail author
  • Pulak Purkait
  • Sanchayan Santra
  • Bhabatosh Chanda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11414)


Mathematical morphological methods have successfully been applied to filter out (emphasize or remove) different structures of an image. However, it is argued that these methods could be suitable for the task only if the type and order of the filter(s) as well as the shape and size of operator kernel are designed properly. Thus the existing filtering operators are problem (instance) specific and are designed by the domain experts. In this work we propose a morphological network that emulates classical morphological filtering consisting of a series of erosion and dilation operators with trainable structuring elements. We evaluate the proposed network for image de-raining task where the SSIM and mean absolute error (MAE) loss corresponding to predicted and ground-truth clean image is back-propagated through the network to train the structuring elements. We observe that a single morphological network can de-rain an image with any arbitrary shaped rain-droplets and achieves similar performance with the contemporary CNNs for this task with a fraction of trainable parameters (network size). The proposed morphological network (MorphoN) is not designed specifically for de-raining and can readily be applied to similar filtering/noise cleaning tasks. The source code can be found here


Mathematical morphology Optimization Morphological network Image filtering 



Initial part of the experiment has been carried out on Intel AI DevCloud. Authors want to acknowledge Intel for that.


  1. 1.
    de A. Araujo, R.: A morphological perceptron with gradient-based learning for Brazilian stock market forecasting. Neural Netw. 28, 61–81 (2012)Google Scholar
  2. 2.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE TPAMI 40(4), 834–848 (2018)CrossRefGoogle Scholar
  3. 3.
    Davidson, J.L., Hummer, F.: Morphology neural networks: An introduction with applications. Circ. Syst. Sig. Process. 12(2), 177–210 (1993)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fu, X., Huang, J., Ding, X., Liao, Y., Paisley, J.: Clearing the skies: a deep network architecture for single-image rain removal. IEEE TIP 26(6), 2944–2956 (2017)MathSciNetGoogle Scholar
  5. 5.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)zbMATHGoogle Scholar
  6. 6.
    Mondal, R., Santra, S., Chanda, B.: Image dehazing by joint estimation of transmittance and airlight using bi-directional consistency loss minimized FCN. In: CVPR Workshops, pp. 920–928 (2018)Google Scholar
  7. 7.
    Mondal, R., Santra, S., Chanda, B.: Dense morphological network: an universal function approximator. arxiv e-prints arXiv:1901.00109, January 2019
  8. 8.
    Perret, B., Cousty, J., Ura, J.C.R., Guimarães, S.J.F.: Evaluation of morphological hierarchies for supervised segmentation. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 39–50. Springer, Cham (2015). Scholar
  9. 9.
    Pessoa, L.F.C., Maragos, P.: Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition. Pattern Recogn. 33, 945–960 (2000)CrossRefGoogle Scholar
  10. 10.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  11. 11.
    Ritter, G.X., Sussner, P.: An introduction to morphological neural networks. In: ICPR, vol. 4, pp. 709–717, August 1996Google Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  13. 13.
    Sussner, P.: Morphological perceptron learning. In: ICRA, pp. 477–482 September 1998Google Scholar
  14. 14.
    Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE TIP 2(2), 176–201 (1993)Google Scholar
  15. 15.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE TIP 13(4), 600–612 (2004)Google Scholar
  16. 16.
    Wdowiak, M., Markiewicz, T., Osowski, S., Swiderska, Z., Patera, J., Kozlowski, W.: Hourglass shapes in rank grey-level hit-or-miss transform for membrane segmentation in HER2/neu images. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 3–14. Springer, Cham (2015). Scholar
  17. 17.
    Zamora, E., Sossa, H.: Dendrite morphological neurons trained by stochastic gradient descent. Neurocomputing 260, 420–431 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ranjan Mondal
    • 1
    Email author
  • Pulak Purkait
    • 2
  • Sanchayan Santra
    • 1
  • Bhabatosh Chanda
    • 1
  1. 1.Indian Statistical InstituteKolkataIndia
  2. 2.The University of AdelaideAdelaideAustralia

Personalised recommendations