Aggregated Contextual Transformations for High-Resolution Image Inpainting (2104.01431v1)

Abstract: State-of-the-art image inpainting approaches can suffer from generating distorted structures and blurry textures in high-resolution images (e.g., 512x512). The challenges mainly drive from (1) image content reasoning from distant contexts, and (2) fine-grained texture synthesis for a large missing region. To overcome these two challenges, we propose an enhanced GAN-based model, named Aggregated COntextual-Transformation GAN (AOT-GAN), for high-resolution image inpainting. Specifically, to enhance context reasoning, we construct the generator of AOT-GAN by stacking multiple layers of a proposed AOT block. The AOT blocks aggregate contextual transformations from various receptive fields, allowing to capture both informative distant image contexts and rich patterns of interest for context reasoning. For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task. Such a training objective forces the discriminator to distinguish the detailed appearances of real and synthesized patches, and in turn, facilitates the generator to synthesize clear textures. Extensive comparisons on Places2, the most challenging benchmark with 1.8 million high-resolution images of 365 complex scenes, show that our model outperforms the state-of-the-art by a significant margin in terms of FID with 38.60% relative improvement. A user study including more than 30 subjects further validates the superiority of AOT-GAN. We further evaluate the proposed AOT-GAN in practical applications, e.g., logo removal, face editing, and object removal. Results show that our model achieves promising completions in the real world. We release code and models in https://github.com/researchmm/AOT-GAN-for-Inpainting.

• The paper introduces the AOT block that aggregates contextual transformations from multiple receptive fields to improve high-res image inpainting.
• It incorporates a mask-prediction task in the discriminator, refining texture synthesis and distinguishing real from inpainted regions.
• Evaluated on the Places2 dataset, the model achieved a 38.60% FID improvement, demonstrating its practical potential in scenarios like logo and object removal.

Aggregated Contextual Transformations for High-Resolution Image Inpainting

The paper "Aggregated Contextual Transformations for High-Resolution Image Inpainting" presents a novel approach to tackle the complex task of image inpainting, specifically focusing on high-resolution images. This work introduces the Aggregated Contextual-Transformation GAN (AOT-GAN), an enhanced GAN-based model designed to improve both context reasoning and texture synthesis when filling large, arbitrarily-shaped missing regions in images.

Key Contributions

1. AOT Block Design: The core innovation is the AOT block which aggregates contextual transformations from multiple receptive fields. By splitting the convolutional kernel into multiple sub-kernels, each applying different dilation rates, the model captures both distant image contexts and diverse patterns, addressing the challenge of context reasoning in high-resolution image inpainting.
2. Enhanced Discriminator with Mask-Prediction Task: The authors introduce a tailored mask-prediction task for the discriminator. This task trains the discriminator to differentiate between real and inpainted textures, helping the generator produce finer, more realistic textures. The discriminator's ability to focus on synthesized regions improves the overall texture clarity.
3. Evaluation and Impact: The model's performance was rigorously tested on the Places2 dataset, a challenging benchmark with 1.8 million high-resolution images. The quantitative results demonstrated a 38.60% improvement in Fréchet Inception Distance (FID) compared to state-of-the-art methods, underscoring its efficacy in realistic image synthesis.
4. Practical Applications: Practical applications of AOT-GAN include logo removal, face editing, and object removal, where the model achieved promising results, illustrating its utility in real-world scenarios.

Implications and Future Directions

The proposed AOT-GAN model exhibits several potential impacts on both theoretical and practical fronts. The modular structure of AOT blocks offers an adaptable approach for other vision tasks requiring high-resolution processing, such as single image super-resolution and image-to-image translation. Future research could explore dynamic or adaptive mechanisms for optimizing AOT block configurations based on specific image features and resolutions. Additionally, integrating advanced object segmentation techniques could further enhance mask selection capabilities, thereby improving the model's robustness in varied applications.

The authors have contributed to advancing high-resolution image inpainting by addressing two critical challenges: effective context reasoning and fine-grained texture synthesis. By leveraging aggregated contextual transformations and a robust discriminator design, the AOT-GAN sets a new standard in the field, open to further exploration and adaptation across related image processing domains.