Image Inpainting with Learnable Bidirectional Attention Maps (1909.00968v3)

Abstract: Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness. Partial convolution has been suggested to address this issue, but it adopts handcrafted feature re-normalization, and only considers forward mask-updating. In this paper, we present a learnable attention map module for learning feature renormalization and mask-updating in an end-to-end manner, which is effective in adapting to irregular holes and propagation of convolution layers. Furthermore, learnable reverse attention maps are introduced to allow the decoder of U-Net to concentrate on filling in irregular holes instead of reconstructing both holes and known regions, resulting in our learnable bidirectional attention maps. Qualitative and quantitative experiments show that our method performs favorably against state-of-the-arts in generating sharper, more coherent and visually plausible inpainting results. The source code and pre-trained models will be available.

• The paper presents a novel learnable bidirectional attention framework that dynamically refines feature transformations for precise image inpainting.
• It integrates encoder and decoder attention maps to distinctly process known and missing regions, effectively handling irregular hole patterns.
• Empirical results on datasets like Paris StreetView and Places demonstrate significant improvements in PSNR and SSIM over existing methods.

Image Inpainting with Learnable Bidirectional Attention Maps: An Overview

The paper presents an advanced method for image inpainting utilizing learnable bidirectional attention maps, aiming to address limitations found in conventional convolutional network (CNN)-based inpainting techniques. These traditional methods often struggle with irregular hole patterns, color inaccuracies, and blurriness due to their indistinguishable treatment of valid pixels and holes. The novel approach detailed in this paper seeks to improve upon these deficiencies by leveraging deep learning techniques that adaptively target fill-in regions through the use of attention mechanisms.

Key Contributions and Methodology

1. Learnable Attention Maps: The core innovation of this paper is the integration of learnable attention maps into the image inpainting process. Traditional methods like partial convolution (PConv) employ a static handcrafted feature re-normalization which may not adequately adapt to varying hole patterns. The proposed approach introduces a learnable module that dynamically alters feature transformations in response to the structure and contour of the holes, effectively capturing semantic consistency.
2. Bidirectional Attention Framework: The paper extends the use of attention maps to both the encoder and decoder of the U-Net architecture. Forward attention maps guide the feature re-normalization in the encoder, while reverse attention maps are introduced for the decoder, allowing it to focus specifically on hole regions. This bidirectional attention mechanism ensures that both the known and unknown areas are processed distinctively, thereby enhancing the inpainting quality.
3. Empirical Improvements: Through comprehensive qualitative and quantitative experiments, the method demonstrates superiority over state-of-the-art techniques, including PatchMatch, Contextual Attention, and PConv. Particularly on datasets like the Paris StreetView and Places, the proposed method exhibits enhanced ability in generating visually coherent and texture-rich outputs, surpassing existing methods in terms of PSNR and SSIM metrics, especially in challenging scenarios with large and irregular gaps.

Results and Implications

The empirical results emphasize the method's effectiveness in producing sharper and more visually congruent images compared to its predecessors. With improvements in resolving fine details and maintaining continuity with surrounding contexts, the method shows promise in practical applications ranging from object removal to restoration of occluded regions.

Future Directions

While the introduction of learnable bidirectional attention maps marks significant progress in the field of image inpainting, the paper opens avenues for further exploration:

• Enhanced Learning Dynamics: Future work could investigate more sophisticated learning mechanisms or optimization techniques to further refine the adaptability and performance of attention maps across diverse datasets and scenarios.
• Integration with Generative Models: Combining the current framework with advanced GAN architectures might yield improvements in generating high-fidelity and diversified image completions.
• Application-specific Adaptations: Tailoring the approach for specific domains, such as medical imaging or satellite image restoration, could benefit from customized learning objectives and network architectures, bolstering the method's applicability in niche areas.

In conclusion, the research presents a valuable contribution to the field of image restoration by resolving notable limitations of previous methods and setting a foundation for continued innovation in deep learning-driven inpainting techniques.