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Related Experiment Video

Updated: Jun 21, 2025

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Image inpainting algorithm based on double curvature-driven diffusion model with P-Laplace operator.

Lifang Xiao1,2, Jianhao Wu1,2

  • 1School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China.

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|July 16, 2024
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Summary
This summary is machine-generated.

This study introduces an improved Curvature-Driven Diffusion (CDD) model using a P-Laplace operator for image inpainting, effectively repairing damaged images with complex textures and noise. The new method enhances visual connectivity and detail restoration, outperforming existing models.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Partial Differential Equations

Background:

  • Traditional Curvature-Driven Diffusion (CDD) models struggle with image inpainting, especially with noisy or distorted images, leading to blurred details and inconsistent global content.
  • Existing methods like Total Variation (TV) models may fail to preserve visual connectivity and leave inpainting traces.

Purpose of the Study:

  • To enhance the Curvature-Driven Diffusion (CDD) model for improved image inpainting performance.
  • To address limitations of existing models in handling complex textures, noise, and global image consistency.

Main Methods:

  • Introduced a P-Laplace operator into the diffusion term of the CDD model to regulate diffusion speed.
  • Discretized the improved CDD model and employed weighted average iterations using surrounding image information.
  • Utilized distance-based weighted averaging to combine iterated images for the final inpainting result.

Main Results:

  • The proposed P-Laplace operator-enhanced CDD model demonstrates superior image inpainting results compared to traditional CDD and TV models.
  • Achieved higher quality restoration in terms of texture structure, visual connectivity, and detail preservation.
  • Experimental results showed significant improvements with peak Signal-to-Noise Ratio (PSNR) up to 38.7982, Structural Similarity Index Measure (SSIM) up to 0.9407, and Feature Similarity Index Measure (FSIM) up to 0.9781.
  • The algorithm effectively removes inpainting traces and requires fewer iterations.

Conclusions:

  • The P-Laplace operator-based CDD model offers a more rational and effective approach to image inpainting.
  • This method significantly improves upon existing techniques, providing better visual quality and objective performance metrics.
  • The enhanced model is efficient and capable of restoring images with complex textures and severe distortions.