Self-Prior Guided Pixel Adversarial Networks for Blind Image Inpainting.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Blind image inpainting requires determining both the corrupted regions and the restoration strategy.
• Existing methods often fail to explicitly address these two critical aspects separately.

Purpose of the Study:

• To propose a novel self-prior guided inpainting network (SIN) that explicitly handles both
• where to inpaint
• and
• how to inpaint
• aspects.
• To leverage self-priors for improved context perception and semantic-aware texture synthesis in image inpainting.

Main Methods:

• The proposed SIN network utilizes self-priors derived from semantic discontinuity detection and global structure prediction.
• Self-priors are integrated into the network for context awareness and to guide texture synthesis.
• Pixel-wise adversarial and high-level semantic structure feedback are employed to ensure semantic continuity.

Main Results:

• The SIN method achieves state-of-the-art performance in both quantitative metrics and visual quality for blind image inpainting.
• The approach demonstrates superiority over methods that assume prior knowledge of corrupted regions.
• Experiments confirm the method's effectiveness across various image restoration tasks.

Conclusions:

• The self-prior guided inpainting network (SIN) offers a robust solution for blind image inpainting by effectively integrating spatial and semantic information.
• The explicit consideration of "where to inpaint" and "how to inpaint" leads to significant improvements in restoration quality.
• The SIN method provides a versatile framework for high-quality image inpainting and related restoration tasks.