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Scale Input Adapted Attention for Image Denoising Using a Densely Connected U-Net: SADE-Net

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

In this work, we address the problem of image denoising using deep neural networks. Recent developments in convolutional neural networks provide a very potent alternative for image restoration applications and in particular for image denoising. A particularly popular deep network structure for image processing are the auto-encoders which include the U-Net as an important example. U-Nets contract and expand feature maps repeatedly, which leads to extraction of multi scale information as well as an increase in the effective receptive field when compared to conventional convolutional nets. In this paper, we propose the integration of a multi scale channel attention module through a U-Net structure as a novelty for the image denoising problem. The introduced network structure also utilizes multi scale inputs in the various substages of the encoder module in a novel manner. Simulation results demonstrate competitive and mostly superior performance when compared to some state of the art deep learning based image denoising methodologies. Qualitative results also indicate that the developed deep network framework has powerful detail preserving capability.

This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under project no. 119E248.

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Acknowledgment

This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under project no. 119E248.

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Authors and Affiliations

  1. Graduate School, Istanbul Technical University, Istanbul, Turkey

    Vedat Acar

  2. Electronics and Communication Engineering Department, Istanbul Technical University, Istanbul, Turkey

    Ender M. Eksioglu

Authors
  1. Vedat Acar
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  2. Ender M. Eksioglu
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Corresponding author

Correspondence to Vedat Acar .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Acar, V., Eksioglu, E.M. (2021). Scale Input Adapted Attention for Image Denoising Using a Densely Connected U-Net: SADE-Net. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_60

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

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