Global and Local Attention-Based Free-Form Image Inpainting

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Deep learning excels at image inpainting for rectangular and irregular holes.
• Irregular hole inpainting is challenging due to shape and location uncertainties.
• Convolutional Neural Networks (CNNs) and adversarial methods alone struggle with irregular holes, requiring attention-based guidance.

Purpose of the Study:

• To develop advanced attention mechanisms for improved irregular hole image inpainting.
• To address the limitations of existing methods in handling complex hole structures.
• To enhance the plausibility and quality of generated inpainting content.

Main Methods:

• Proposed two novel attention mechanisms: a mask pruning-based global attention module and a global and local attention module.
• These modules capture global dependency and local similarity information from features.
• The method integrates these attention mechanisms into a deep learning framework for image inpainting.

Main Results:

• The proposed method demonstrated superior performance compared to state-of-the-art techniques.
• Quantitative and qualitative evaluations confirmed the effectiveness of the new attention modules.
• The approach successfully generated refined inpainting results for irregular holes.

Conclusions:

• The novel attention mechanisms significantly improve deep learning-based image inpainting for irregular holes.
• Integrating global and local feature information is crucial for handling complex inpainting tasks.
• The proposed method offers a more robust and accurate solution for image restoration challenges.