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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)

Abstract

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 https://github.com/ranjanZ/2D-Morphological-Network.

Keywords

Mathematical morphology Optimization Morphological network Image filtering 

Notes

Acknowledgements

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

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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

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