Regionwise Generative Adversarial Image Inpainting for Large Missing Areas.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Deep neural networks show promise for image inpainting but struggle with large missing regions, causing artifacts.
• Existing methods often fail with large contiguous missing areas, limiting their practical application.

Purpose of the Study:

• To develop a generic image inpainting framework for handling both contiguous and discontiguous large missing regions.
• To improve the semantic reasonableness and visual realism of restored images.

Main Methods:

• Proposed a regionwise generative adversarial framework, applying separate operations for existing and missing image regions.
• Introduced a correlation loss to capture nonlocal patch correlations for enhanced information retrieval during inference.

Main Results:

• The framework successfully restored semantically reasonable and visually realistic images for both types of large missing areas.
• Extensive experiments demonstrated superior performance over state-of-the-art approaches on widely used inpainting datasets.

Conclusions:

• The proposed regionwise adversarial mechanism and correlation loss effectively address limitations in current image inpainting techniques.
• This framework offers a significant advancement in restoring images with extensive missing content.