Progressive Contextual Aggregation Empowered by Pixel-Wise Confidence Scoring for Image Inpainting

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Traditional image inpainting methods struggle with large missing regions, leading to visual artifacts.
• Inferring pixel information in deep holes is challenging due to limited surrounding context.

Purpose of the Study:

• To develop an advanced image inpainting technique capable of handling large corrupted areas effectively.
• To enhance the realism and quality of inpainted images by addressing limitations of existing methods.

Main Methods:

• A progressive hole-filling scheme operating hierarchically in feature and image spaces.
• A pixel-wise dense detector to distinguish masked regions and guide the generator.
• A structure transfer module (STM) for merging multi-resolution completed images, incorporating local and global interactions.

Main Results:

• The proposed method demonstrates significantly improved visual quality compared to state-of-the-art techniques.
• Effective handling of large holes without introducing noticeable visual artifacts.
• Enhanced realism in completed image regions through detailed pixel-wise analysis.

Conclusions:

• The hierarchical progressive inpainting approach offers superior performance for challenging image completion tasks.
• The integration of a dense detector and STM module contributes to high-fidelity image reconstruction.
• This method provides a robust solution for generating visually convincing results even with extensive image corruption.