Semantic Image Inpainting with Deep Generative Models (1607.07539v3)

Abstract: Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.

Semantic Image Inpainting with Deep Generative Models

Overview

The paper "Semantic Image Inpainting with Deep Generative Models" by Yeh et al. presents a novel approach to the challenging task of semantic image inpainting. Unlike classical inpainting techniques, which heavily rely on local or non-local information to restore images, this method leverages deep generative models, specifically GANs, to generate missing content based on the available data. The paper highlights several limitations of existing methods and introduces a solution that overcomes these limitations by considering the semantic context of the image.

Methodology

The primary contribution of the work is a method that formulates semantic inpainting as a constrained image generation problem. The authors propose using a deep generative model trained on a dataset of uncorrupted images to infer missing content. The approach involves searching for the closest encoding of the corrupted image in the latent space of the generative model. This encoding is found by minimizing a weighted context loss and a prior loss:

1. Context Loss: This loss ensures that the generated image is consistent with the uncorrupted parts of the input image. The paper introduces an importance-weighted context loss to emphasize regions close to the missing parts.
2. Prior Loss: This loss penalizes unrealistic outputs by making use of the discriminator in a GAN framework. By incorporating the GAN's discriminator, the generated images are more likely to be photorealistic.

Results

The experimental results demonstrate the efficacy of the proposed method on three datasets: CelebA, SVHN, and Stanford Cars. The method shows significant improvements over state-of-the-art techniques, such as the Context Encoder (CE) method:

• Photorealism: The proposed method produces sharper and more realistic images compared to the CE method, which tends to generate blurry and less plausible content when dealing with arbitrary shaped missing regions.
• Flexibility: Unlike CE, which requires specific mask patterns during training, the proposed approach can handle arbitrary missing regions without the need for re-training the network.

The paper presents qualitative results, showcasing the generated images for different types of masks, including central blocks, random patterns, and large missing regions. Quantitatively, although the proposed method might show lower PSNR values compared to CE due to the non-unique nature of the ground truth, the perceptual quality of the results is superior.

Implications and Future Work

The research introduces an effective technique for dealing with large missing regions in images and suggests several practical applications, such as image editing and restoration of damaged artworks. The implications of this work are significant for fields where photorealistic image synthesis is crucial. Moreover, the ability to handle arbitrarily structured missing regions without specialized training sets a new standard in the inpainting domain.

From a theoretical perspective, the integration of GANs with back-propagation to the input provides an innovative way to navigate the latent space, opening avenues for further exploration in other image manipulation tasks. Future developments could involve enhancing the generative model's robustness and extending the approach to more complex datasets and higher resolution images.

Furthermore, future research could focus on improving the optimization process, potentially exploring alternative loss functions or regularization techniques to further enhance the realism of the output images. Integrating newer advancements in generative models, such as those involving transformer-based architectures, could also yield significant improvements.

Conclusion

The paper by Yeh et al. makes a compelling contribution to the field of semantic image inpainting. By leveraging deep generative models and innovatively defining loss functions, the proposed method achieves superior performance in generating realistic images from heavily corrupted inputs. The work showcases both practical advantages and theoretical advancements, suggesting a promising direction for future research in image restoration and manipulation tasks.