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Inpainting with Separable Mask Update Convolution Network.

Jun Gong1, Senlin Luo1, Wenxin Yu2

  • 1Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing 100081, China.

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|August 12, 2023
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Summary
This summary is machine-generated.

This study introduces a new separable mask update convolution for image inpainting, effectively restoring images with large missing areas by intelligently handling invalid data. The method improves image quality and reduces model size.

Keywords:
encoder-decoder networkimage inpaintingimage processingseparable mask update convolution

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Image inpainting aims to reconstruct missing image regions.
  • Deep learning has advanced image restoration.
  • Existing methods struggle with large missing areas due to invalid data.

Purpose of the Study:

  • To propose a novel image inpainting method for large missing areas.
  • To address the challenge of invalid information interference.
  • To improve restoration quality and model efficiency.

Main Methods:

  • Separable mask update convolution automatically learns and updates masks.
  • This reduces network parameters and model size.
  • Regional normalization enhances feature extraction.

Main Results:

  • The proposed method effectively restores images with large missing areas.
  • It outperforms state-of-the-art image inpainting techniques.
  • Significant improvements in image quality were observed.

Conclusions:

  • The separable mask update convolution is a promising approach for image inpainting.
  • The method offers enhanced restoration quality and efficiency.
  • It successfully mitigates issues caused by invalid data in large missing regions.