Foreground-aware Image Inpainting (1901.05945v3)

Abstract: Existing image inpainting methods typically fill holes by borrowing information from surrounding pixels. They often produce unsatisfactory results when the holes overlap with or touch foreground objects due to lack of information about the actual extent of foreground and background regions within the holes. These scenarios, however, are very important in practice, especially for applications such as the removal of distracting objects. To address the problem, we propose a foreground-aware image inpainting system that explicitly disentangles structure inference and content completion. Specifically, our model learns to predict the foreground contour first, and then inpaints the missing region using the predicted contour as guidance. We show that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inpainting. Experiments show that our method significantly outperforms existing methods and achieves superior inpainting results on challenging cases with complex compositions.

• The paper demonstrates that integrating contour prediction with image inpainting significantly improves spatial awareness and texture coherence.
• It introduces a hierarchical framework with distinct modules for incomplete contour detection, contour completion, and guided image completion.
• Empirical results show enhanced performance in L1, L2, PSNR, and SSIM metrics, underscoring its potential for advanced image editing applications.

Foreground-aware Image Inpainting: A Structured Approach to Complex Image Completion Tasks

The paper "Foreground-aware Image Inpainting" introduces a novel image inpainting technique that specifically addresses challenges arising when inpainting tasks involve holes overlapping with foreground objects. Traditional methods typically fall short in these scenarios due to insufficient discrimination between the image's foreground and background within the holes. The authors propose a comprehensive system that disentangles structure inference from content completion, leveraging contour prediction to guide the inpainting process.

Methodology and Contributions

The core methodology of this research encompasses the systematic use of hierarchical processing through three primary modules: the incomplete contour detection module, the contour completion module, and the image completion module. This structured approach allows for the explicit modeling of foreground contours, which evenly propagates into a more informed inpainting process.

1. Contour Detection and Completion: The authors employ a contour detection system using DeepCut, which is pre-trained for effective segmentation, despite any holes present in the input images. The contour completion module is designed as a generator with a dual-network architecture for refined prediction. By leveraging adversarial training, the authors ensure sharp and coherent contour completion, mitigating issues often exacerbated by sparsity in contour data.
2. Image Completion with Contour Guidance: Contrary to previous end-to-end masked pixel prediction methods, this paper integrates completed contours into the image completion process. The enhanced input allows the model to achieve superior spatial awareness and texture coherence, particularly around the boundaries of inpainted regions. The efficiency of this contour-guided image completion process is supported by empirical results, revealing significant quantitative improvements across standard measures such as L1, L2, PSNR, and SSIM.

Implications and Future Directions

The implications of this work are twofold: on an applied level, it advances high-fidelity image editing tools capable of sophisticated object removal and background repair. Theoretically, it suggests a promising avenue for structured learning in image-based tasks, where representations of structural information (like contours) are used as pivotal components of the learning process.

Future developments could see the extension of this methodology to more generalized semantic understanding tasks or to scenarios involving complex object interactions. Additionally, integrating this approach with larger, more diverse datasets could enhance its robustness further. Higher-order understanding of textures and materials might also be explored, building upon this work's foundation to inform models that might autonomously reason about scene context and object interactions.

In conclusion, "Foreground-aware Image Inpainting" lays the groundwork for structured enhancements in image inpainting strategies, challenging the prevailing paradigms and showcasing the advantages of explicitly encoding categorical knowledge within generative models. This work stands to influence subsequent research in artificial intelligence-driven image processing methodologies.