MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting (2203.06304v1)

Abstract: Although achieving significant progress, existing deep generative inpainting methods are far from real-world applications due to the low generalization across different scenes. As a result, the generated images usually contain artifacts or the filled pixels differ greatly from the ground truth. Image-level predictive filtering is a widely used image restoration technique, predicting suitable kernels adaptively according to different input scenes. Inspired by this inherent advantage, we explore the possibility of addressing image inpainting as a filtering task. To this end, we first study the advantages and challenges of image-level predictive filtering for image inpainting: the method can preserve local structures and avoid artifacts but fails to fill large missing areas. Then, we propose semantic filtering by conducting filtering on the deep feature level, which fills the missing semantic information but fails to recover the details. To address the issues while adopting the respective advantages, we propose a novel filtering technique, i.e., Multilevel Interactive Siamese Filtering (MISF), which contains two branches: kernel prediction branch (KPB) and semantic & image filtering branch (SIFB). These two branches are interactively linked: SIFB provides multi-level features for KPB while KPB predicts dynamic kernels for SIFB. As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting. We validate our method on three challenging datasets, i.e., Dunhuang, Places2, and CelebA. Our method outperforms state-of-the-art baselines on four metrics, i.e., L1, PSNR, SSIM, and LPIPS. Please try the released code and model at https://github.com/tsingqguo/misf.

• The paper presents a novel formulation that reframes image inpainting as a dynamic filtering task integrating both pixel and semantic levels.
• It introduces a dual-branch architecture that synergistically combines kernel prediction and semantic content filtering to reduce artifacts.
• Evaluations on benchmark datasets demonstrate superior performance over state-of-the-art methods in metrics such as PSNR and SSIM.

An Expert Overview of Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

The paper introduces a novel approach to image inpainting through a method called Multi-level Interactive Siamese Filtering (MISF). This approach addresses the limitations of existing deep generative inpainting methods, which often struggle with generalization across varied scenes and result in image artifacts or discrepancies in filled pixels relative to ground truth.

Key Contributions

1. Formulation of Inpainting as a Filtering Task: The authors reframe image inpainting as a predictive filtering task. Traditional inpainting techniques have leveraged deep generative models but failed to address specific challenges of inpainting such as preserving local structures while filling large missing areas. The incorporation of image-level predictive filtering is aimed at adapting dynamically to different input scenes by predicting optimal kernels, thus preserving local structures and minimizing artifacts.
2. Introduction of Semantic Filtering: To overcome the shortcomings of basic predictive filtering, which struggles with large missing areas, the paper proposes semantic filtering. Conducted at a deep feature level, it can fill in missing semantic information though often at the cost of losing finer details.
3. Development of MISF: MISF is presented as a culmination of the benefits of both image-level and semantic filtering. By implementing two interactively linked branches—a kernel prediction branch (KPB) and a semantic content image filtering branch (SIFB)—the method effectively integrates multi-level features and dynamically predicted kernels. The KPB utilizes input along with the multi-level features from the SIFB to predict these kernels, synergizing semantic image filling to achieve high-fidelity outcomes.

Theoretical and Practical Implications

MISF is a significant development offering a new methodology in which inpainting can be envisioned as a combination of deep learning and conventional filtering methods. The technique excels particularly in its ability to perform dynamic convolution operations, modeled to adapt to both semantic and pixel-level data, thus offering promising potential in varied applications where image restoration is crucial.

The algorithm's high generalization capabilities are validated on challenging datasets—Dunhuang, Places2, and CelebA—where it outperforms existing state-of-the-art models across multiple metrics, namely $L_1$, PSNR, SSIM, and LPIPS. The interactive Siamese filtering may pave the way for future research, where dynamic predictive models are further harnessed to tackle complex vision tasks.

Future Directions

This work opens up several avenues for advanced research in image restoration tasks leveraging predictive filtering. Future explorations could focus on refining the architectures of predictive networks employed within such systems to enhance performance further. Additionally, exploring the applicability of MISF beyond the scope of inpainting—possibly into other domains of image processing such as super-resolution, deblurring, or even video frames interpolation—could yield substantial advancements.

In conclusion, this paper presents MISF as a robust solution to existing challenges in image inpainting, demonstrating superiority through both extensive quantitative metrics assessments and visual validations. As predictive filtering frameworks evolve, the integration of semantic understanding in tandem with low-level image processing will likely become a focal point in the development of future intelligent vision systems.