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Image Denoising Using Adaptive and Overlapped Average Filtering and Mixed-Pooling Attention Refinement Networks

Author

Listed:
  • Ming-Hao Lin

    (Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • Zhi-Xiang Hou

    (Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan)

  • Kai-Han Cheng

    (Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan)

  • Chin-Hsien Wu

    (Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • Yan-Tsung Peng

    (Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan)

Abstract

Cameras are essential parts of portable devices, such as smartphones and tablets. Most people have a smartphone and can take pictures anywhere and anytime to record their lives. However, these pictures captured by cameras may suffer from noise contamination, causing issues for subsequent image analysis, such as image recognition, object tracking, and classification of an object in the image. This paper develops an effective combinational denoising framework based on the proposed Adaptive and Overlapped Average Filtering (AOAF) and Mixed-pooling Attention Refinement Networks (MARNs). First, we apply AOAF to the noisy input image to obtain a preliminarily denoised result, where noisy pixels are removed and recovered. Next, MARNs take the preliminary result as the input and output a refined image where details and edges are better reconstructed. The experimental results demonstrate that our method performs favorably against state-of-the-art denoising methods.

Suggested Citation

  • Ming-Hao Lin & Zhi-Xiang Hou & Kai-Han Cheng & Chin-Hsien Wu & Yan-Tsung Peng, 2021. "Image Denoising Using Adaptive and Overlapped Average Filtering and Mixed-Pooling Attention Refinement Networks," Mathematics, MDPI, vol. 9(10), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1130-:d:555886
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    Cited by:

    1. Josep Arnal & Luis Súcar, 2022. "Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images," Mathematics, MDPI, vol. 10(19), pages 1-18, October.

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