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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Progressively Inpainting Images Based on a Forked-Then-Fused Decoder Network.

Shuai Yang1, Rong Huang1,2, Fang Han1,2

  • 1College of Information Science and Technology, Donghua University, Shanghai 201620, China.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces a novel progressive image inpainting method using a forked-then-fused decoder. The approach effectively fills corrupted image regions with realistic content, outperforming existing methods.

Keywords:
contextual attentionfeature fusionimage inpaintingmulti-stage

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Image inpainting is crucial for restoring corrupted image regions.
  • Existing methods struggle with generating semantically plausible and visually realistic content.

Purpose of the Study:

  • To propose a progressive image inpainting method.
  • To enhance the quality and plausibility of inpainted image regions.

Main Methods:

  • Developed a forked-then-fused decoder network.
  • Utilized Partial Convolution-Region Normalization (PC-RN) units for feature extraction.
  • Incorporated multi-scale contextual attention modules and a progressive inpainting strategy.

Main Results:

  • The proposed method achieved superior performance on Places2, Paris StreetView, and CelebA datasets.
  • Qualitative and quantitative evaluations demonstrated the model's effectiveness.
  • Ablation studies confirmed the contribution of individual modules.

Conclusions:

  • The novel progressive image inpainting method significantly improves content restoration.
  • The forked-then-fused decoder and PC-RN units are effective components for inpainting.
  • The approach offers a robust solution for generating realistic and plausible image completions.