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Image Denoising Using a Deep Encoder-Decoder Network with Skip Connections

  • Raphaël Couturier
  • Gilles Perrot
  • Michel Salomon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

In many areas images can be corrupted by various types of noise and therefore image denoising is a prerequisite. For example, medical images like the 4D-CT or ultrasound ones, are prone to noise and artifacts that can affect diagnostic confidence. Remote sensing is another field for which image preprocessing is mandatory to improve the quality of source images. Synthetic Aperture Radar (SAR) images are typically corrupted by multiplicative speckle noise. In this paper, a deep neural network able to deal with both additive white Gaussian and multiplicative speckle noises is developed, showing also some blind denoising capacity. The experiments on noisy images show that the proposal, which consists in a encoder-decoder, is efficient and competitive in comparison with state-of-the-art methods.

Keywords

Image denoising Additive and multiplicative noises Deep learning Encoder-decoder 

Notes

Acknowledgment

This work has been supported by the EIPHI Graduate School (contract “ANR-17-EURE-0002”).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Raphaël Couturier
    • 1
  • Gilles Perrot
    • 1
  • Michel Salomon
    • 1
  1. 1.FEMTO-ST Institute, CNRS - Univ. Bourgogne Franche-Comté (UBFC)BelfortFrance

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