Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting (1904.07475v4)

Abstract: High-quality image inpainting requires filling missing regions in a damaged image with plausible content. Existing works either fill the regions by copying image patches or generating semantically-coherent patches from region context, while neglect the fact that both visual and semantic plausibility are highly-demanded. In this paper, we propose a Pyramid-context ENcoder Network (PEN-Net) for image inpainting by deep generative models. The PEN-Net is built upon a U-Net structure, which can restore an image by encoding contextual semantics from full resolution input, and decoding the learned semantic features back into images. Specifically, we propose a pyramid-context encoder, which progressively learns region affinity by attention from a high-level semantic feature map and transfers the learned attention to the previous low-level feature map. As the missing content can be filled by attention transfer from deep to shallow in a pyramid fashion, both visual and semantic coherence for image inpainting can be ensured. We further propose a multi-scale decoder with deeply-supervised pyramid losses and an adversarial loss. Such a design not only results in fast convergence in training, but more realistic results in testing. Extensive experiments on various datasets show the superior performance of the proposed network

Pyramid-context Encoder Network for Image Inpainting

The paper describes a novel architecture, the Pyramid-context Encoder Network (PEN-Net), which innovatively addresses the problem of high-quality image inpainting using deep generative models. Image inpainting is a task focused on filling in missing regions of an image in a way that is both visually plausible and semantically coherent. The approach is explicitly designed to overcome limitations observed in existing methodologies, such as the tendency to compromise either visual detail or semantic accuracy.

Key Innovations and Methodology

PEN-Net is built upon the U-Net structure, known for its effectiveness in tasks requiring precise localization and context encoding. It leverages three core innovations:

1. Pyramid-context Encoder: This component is integrated to enhance the encoder's ability by progressively filling missing image sections at multiple levels of resolution and semantic understanding. The process involves "attention transfer," where region affinity is learned at higher levels and used to fill gaps at lower resolution levels, enriching the accuracy of details and semantics.
2. Attention Transfer Network (ATN): ATN learns region affinity, leveraging patch-based attention mechanisms from deep layers to guide feature reconstruction in shallower layers. It employs dilated convolutions to capture multi-scale contextual information for texture coherence.
3. Multi-scale Decoder with Adversarial Losses: The multi-scale decoder refines image reconstruction across several scales and resolutions, optimizing with deeply-supervised pyramid L1 losses in conjunction with adversarial losses. This dual-objective training paradigm converges more efficient and realistic component recovery outcomes, particularly when generating fine details in complex textures and natural scenes.

Empirical Evaluation

The paper presents extensive experiments across four distinguished datasets: Facade, DTD (texture dataset), CELEBA-HQ (face dataset), and Places2 (scene dataset), each exhibiting distinct structural and texture-based challenges. Performance metrics include L1 loss, Multiscale-SSIM (MS-SSIM) for structural comparisons, Inception Score (IS), and Fréchet Inception Distance (FID). The proposed model demonstrates superior quantitative results, particularly in terms of FID and MS-SSIM, suggesting high fidelity to structurally and visually coherent completions.

Results and Implications

Qualitative evaluations highlight PEN-Net's capability to synthesize missing image regions that harmoniously blend into their surrounding context, maintaining both visual authenticity and semantic integrity. Notably, its cross-layer attention transfer approach provides sharper and more contextually accurate inpainting results compared to traditional patch-based or standard generative models.

The implications of PEN-Net extend beyond artistic restoration into practical domains such as photo editing, autonomous scene understanding, and even medical imaging, where plausible reconstruction of missing data is crucial. The technique enables robust image completion without relying exclusively on pre-existing patches, offering considerable flexibility and adaptation to diverse and unpredictable image data.

Future Directions

The paper concludes by identifying potential research avenues, notably extending PEN-Net’s capabilities for higher-resolution images and complex mask patterns. Moreover, exploring the integration of unsupervised and reinforcement learning could pave the way for even more adaptive and intelligent image completion strategies.

In summary, the PEN-Net framework represents a substantial step forward in image inpainting, presenting a cohesive model that adeptly balances between preserving intricate image details and maintaining semantic relevance, setting a benchmark for subsequent advancements in automated image reconstruction.