HRC-Net: Learning Visual Hypothesis, Representative, and Collaboration for Multi-Domain Image Inpainting

Multi-domain image inpainting utilizes complementary contextual information from auxiliary domain images to restore corrupted regions. While existing methods reconstruct auxiliary images to provide additional guidance, they face fundamental limitations: recovered pixels with complex patterns often lack representative details, while oversimplified patterns offer insufficient contextual information. To address these challenges, we propose HRC-Net, a novel framework incorporating three generative sub-networks for the comprehensive image inpainting task. Our architecture consists of: (1) A Hypothesis Sub-network that enables robust samplings of pixel-wise hypotheses from multi-domain inputs; (2) A Representative Sub-network that learns to score hypothesis quality based on contextual relevance; and (3) a Collaboration Sub-network that optimizes adaptive fusion kernels to integrate the most pertinent details. Together, these components model the joint distribution of representative scores and convolutional kernels, fostering a precise interaction between auxiliary hypotheses and target image corruption to meticulously repair the target image. Extensive evaluations across multiple benchmark datasets demonstrate HRC-Net's superior performance, significantly outperforming state-of-the-art methods in both quantitative metrics and visual quality.