High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis (1611.09969v2)

Abstract: Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.

• The paper introduces a joint optimization framework that integrates global content constraints from an encoder-decoder CNN with local neural patch synthesis for realistic inpainting.
• It employs a multi-scale, coarse-to-fine strategy to progressively refine details, demonstrating improved L1/L2 losses and higher PSNR on datasets like Paris StreetView.
• Empirical evaluations show superior performance over methods such as Context Encoder and PatchMatch, highlighting its potential for advanced image editing tasks.

High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

Overview of the Paper

The paper "High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis" by Yang et al. explores the challenges and implementation of an advanced method for image inpainting. The authors propose a hybrid approach that leverages the combined strengths of structured prediction by an encoder-decoder Convolutional Neural Network (CNN) and the fine texture synthesis capabilities of neural patches. This method addresses the limitations observed in prior deep learning and traditional texture synthesis approaches when applied to high-resolution images.

Key Contributions

The paper introduces three major contributions to the field of image inpainting:

1. Joint Optimization Framework: The paper puts forth a framework that integrates global content constraints derived from a trained CNN and local texture constraints modeled via neural patches. This hybrid approach enables the synthesis of plausible high-frequency textures within the inpainted regions.
2. Multi-Scale Neural Patch Synthesis Algorithm: Extending the joint optimization framework, the authors introduce a multi-scale formulation that efficiently handles high-resolution images. This algorithm operates in a coarse-to-fine manner, progressively refining the inpainted content at multiple scales.
3. Application of Mid-Layer Neural Features for Realistic Texture Synthesis: The paper demonstrates that features from mid-layers of a neural network, traditionally used for style transfer tasks, can be effectively repurposed to generate detailed, contextually coherent textures for inpainting tasks.

Methodology

The proposed method initializes inpainting with a content prediction network, a variant of the Context Encoder, trained under combined $\ell_2$ and adversarial losses. For handling high-resolution images, the authors construct a multi-scale pyramid and perform inpainting in successive refinements from lower to higher resolutions. At each scale, a joint optimization problem is solved, incorporating both the content and texture constraints. The texture constraints focus on ensuring local neural patches within the hole region resemble those of the boundaries.

Empirical Evaluation

The efficacy of the proposed method was quantitatively and qualitatively validated on the ImageNet and Paris Streetview datasets. The authors showcased significant improvements over baseline methods, including Context Encoder and Content-Aware Fill (PatchMatch), particularly for preserving high-frequency details and achieving seamless integration of the inpainted regions.

Numerical Results

Quantitative results in the paper reveal a notable performance enhancement in terms of L1 and L2 losses and Peak Signal-to-Noise Ratio (PSNR). Specifically, the proposed technique achieved consistently lower mean L1 and L2 losses, along with higher PSNR values, compared to competing methods on the Paris StreetView dataset.

Implications and Future Directions

The practical implications of this research are considerable for diverse image editing tasks such as object removal and scene enhancement. Theoretically, the paper substantiates the potency of intermediate neural layers in rendering fine details—paving the way for further explorations in image synthesis and manipulation. However, certain limitations such as optimization speed and occasional artifacts in complex scenes remain.

Future work could focus on improving the computational efficiency of the algorithm and extending the framework to other application domains like image super-resolution, denoising, and view synthesis. Enhancing robustness to intricate image structures and exploring alternative network architectures for the texture network could also yield substantial advancements.

Conclusion

Yang et al. make a noteworthy contribution to the field of image inpainting by fusing neural patch synthesis with a multi-scale joint optimization framework. The proposed method marks a significant step forward in generating high-quality, coherent inpainted regions, demonstrating both theoretical depth and practical utility.