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A novel 8-connected Pixel Identity GAN with Neutrosophic (ECP-IGANN) for missing imputation.

Gamal M Mahmoud1, Mostafa Elbaz2, Fayez Alqahtani3

  • 1Department of Electrical Engineering, Pharos University in Alexandria, Alexandria, Egypt.

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Summary

This study introduces Enhanced Connected Pixel Identity GAN with Neutrosophic (ECP-IGANN) for improved missing pixel imputation in images. The novel model enhances image restoration and segmentation accuracy across diverse datasets.

Keywords:
Data imputationGANsIdentity blockMissing pixelMissing pixel imputationMode collapseNeutrosophic

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Missing pixel imputation is a key challenge in image restoration and inpainting.
  • Existing Generative Adversarial Network (GAN) architectures suffer from mode collapse and lack of pixel integrity.
  • Accurate reconstruction of missing pixel values is crucial for complete visual information.

Purpose of the Study:

  • To introduce a novel model, Enhanced Connected Pixel Identity GAN with Neutrosophic (ECP-IGANN), for accurate missing pixel imputation.
  • To address mode collapse and enhance pixel integrity in GAN-based image reconstruction.
  • To improve image restoration and segmentation performance using the proposed imputation model.

Main Methods:

  • Integration of an identity block in the GAN generator to preserve existing pixel values.
  • Calculation of 8-connected neighboring pixel values to enhance coherence of imputed pixels.
  • Rigorous evaluation on five diverse datasets: BigGAN-ImageNet, 2024 Medical Imaging Challenge, Autonomous Vehicles, 2024 Satellite Imagery, and Fashion and Apparel Dataset 2024.

Main Results:

  • ECP-IGANN demonstrated significant improvements in Inception Score (IS) and Fréchet Inception Distance (FID), indicating enhanced diversity and reduced mode collapse.
  • Marked enhancement in image segmentation performance across all tested datasets.
  • Substantial improvements in Dice Score, Accuracy, Precision, and Recall for segmentation models like Spatial Attention U-Net, Dense U-Net, and Residual Attention U-Net.

Conclusions:

  • ECP-IGANN effectively overcomes limitations of existing GANs for missing pixel imputation.
  • The model shows robust generalizability and significantly boosts performance in image restoration and segmentation tasks.
  • ECP-IGANN offers a promising solution for applications requiring high-fidelity image reconstruction.