Image Fine-grained Inpainting (2002.02609v2)

Abstract: Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently. However, most of them often suffered from completed results with unreasonable structure or blurriness. To mitigate this problem, in this paper, we present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. Benefited from the property of this network, we can more easily recover large regions in an incomplete image. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss for concentrating on uncertain areas and enhancing the semantic details. Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones. We also employ a discriminator with local and global branches to ensure local-global contents consistency. To further improve the quality of generated images, discriminator feature matching on the local branch is introduced, which dynamically minimizes the similarity of intermediate features between synthetic and ground-truth patches. Extensive experiments on several public datasets demonstrate that our approach outperforms current state-of-the-art methods. Code is available at https://github.com/Zheng222/DMFN.

• The paper presents a Dense Multi-scale Fusion Network that uses dense dilated convolutions to significantly enhance inpainting quality.
• It introduces unique loss functions, including Self-guided Regression and Geometrical Alignment Constraints, to improve detail recovery and semantic coherence.
• Experimental results show superior performance on metrics like LPIPS, PSNR, and SSIM across datasets such as CelebA-HQ, Places2, and FFHQ.

Analysis of "Image Fine-grained Inpainting"

In "Image Fine-grained Inpainting," the authors present an innovative approach toward enhancing image inpainting through a single-stage generative network that incorporates dense combinations of dilated convolutions. This methodology is formulated to address common issues seen in image inpainting tasks, such as producing results with unrealistic structures or noticeable blurriness. The paper provides a technically robust approach, which is demonstrated using impressive quantitative and qualitative results surpassing existing state-of-the-art methods.

Methodological Advancements

The proposed model, referred to as the Dense Multi-scale Fusion Network (DMFN), integrates several innovative components. The key architecture of this model, the Dense Multi-scale Fusion Block (DMFB), employs dense combinations of dilated convolutions. This structure strategically extends the receptive fields while maintaining a manageable parameter count, significantly improving over previous approaches that suffered from either sparse feature extraction or excessive computational demands.

Loss Functions

The authors fortify their model with unique loss functions that facilitate the generation of realistic and semantically consistent images. A Self-guided Regression Loss centers high attention on uncertain areas, promoting fine detail recovery. Furthermore, the Geometrical Alignment Constraint introduced an alignment element between generated and target image high-level features, focusing on meticulous semantic spatial arrangement. These enhancements in the loss functions are crucial, as they address the nuances of image synthesis that other models might overlook.

Discriminator Design

The paper adopts a discriminator with dual local and global considerations, intended to ensure consistency in both small-scale texture and broader semantic content. In particular, the discriminator leverages a novel feature matching process on the local branch, which effectively reduces discrepancies between generated patches and their ground-truth counterparts. The choice of using the Relativistic Average GAN (RaGAN) further pushes the model's capabilities, providing more sophisticated adversarial feedback that optimally guides the network training.

Experimental Results

The results presented in the paper strongly support the model's efficacy, demonstrating superior performance across multiple public image datasets, such as CelebA-HQ, Places2, and FFHQ. Quantitatively, the DMFN outperforms existing frameworks in terms of metrics like LPIPS, PSNR, and SSIM, with a marked improvement in image realism. Qualitative assessments also show that the DMFN's outputs are of higher fidelity, retaining critical structural and textural details that contribute to overall image quality.

Implications and Future Work

This paper's outcomes have notable implications in domains that require high-quality image inpainting, such as digital media editing, computer vision tasks in autonomous systems, and even medical imaging. The fine-tuned granularity of the DMFN's output aligns with the increasing demand for high-resolution and semantically consistent image processing tools. Future developments could explore further scaling the model for real-time applications or extending it to handle video completion tasks, potentially using the same architectural principles for temporal coherence.

In conclusion, "Image Fine-grained Inpainting" delivers significant methodological advancements in image inpainting. It efficiently combines innovative network design with enhanced loss functions to produce results that are empirically demonstrated to outperform existing methods. This research contributes meaningfully to the field, both theoretically and practically, as it proposes feasible paths toward solving recurring issues in generative modeling applications.