Coherent Semantic Attention for Image Inpainting (1905.12384v3)

Abstract: The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image. However, the existing methods often generate contents with blurry textures and distorted structures due to the discontinuity of the local pixels. From a semantic-level perspective, the local pixel discontinuity is mainly because these methods ignore the semantic relevance and feature continuity of hole regions. To handle this problem, we investigate the human behavior in repairing pictures and propose a fined deep generative model-based approach with a novel coherent semantic attention (CSA) layer, which can not only preserve contextual structure but also make more effective predictions of missing parts by modeling the semantic relevance between the holes features. The task is divided into rough, refinement as two steps and model each step with a neural network under the U-Net architecture, where the CSA layer is embedded into the encoder of refinement step. To stabilize the network training process and promote the CSA layer to learn more effective parameters, we propose a consistency loss to enforce the both the CSA layer and the corresponding layer of the CSA in decoder to be close to the VGG feature layer of a ground truth image simultaneously. The experiments on CelebA, Places2, and Paris StreetView datasets have validated the effectiveness of our proposed methods in image inpainting tasks and can obtain images with a higher quality as compared with the existing state-of-the-art approaches.

• The paper introduces a novel Coherent Semantic Attention layer to enhance image inpainting with improved texture fidelity and structural consistency.
• The model employs a two-stage U-Net architecture with rough and refinement networks that iteratively reconstruct missing regions.
• Experimental results on CelebA, Places2, and Paris StreetView datasets show superior performance with higher SSIM and PSNR metrics.

Coherent Semantic Attention for Image Inpainting

The paper "Coherent Semantic Attention for Image Inpainting" presents a novel methodology for enhancing image inpainting using a two-step deep generative model integrated with a new layer termed Coherent Semantic Attention (CSA). The inpainting task is profoundly challenging due to the necessity of reconstructing not only missing regions with plausible hypotheses but also maintaining intricate texture details and coherent global structures.

Methodological Novelty

The authors introduce the CSA layer to address the shortcomings of previous methods that often resulted in blurry textures and distorted structures due to discontinuities in local pixel arrangements. The CSA layer innovatively models the semantic relevance between features in the hole regions, thereby preserving contextual structure and enhancing texture predictions. The proposed approach is built on a two-stage process: a rough network and a refinement network, both leveraging the U-Net architecture.

In the rough network stage, initial approximations of missing image regions are made. In contrast, the refinement network utilizes the CSA layer embedded within its encoder to enhance these approximations by modeling semantic correlations across missing regions, thereby ensuring continuity and coherence across features.

Experimental Validation

The paper's experimental section presents a thorough comparison of the CSA-enabled model against several state-of-the-art methodologies, including Contextual Attention, Shift-net, Partial Conv, and Gated Conv. Evaluation metrics include L1, L2 loss, along with SSIM and PSNR for image quality assessment, underscoring the CSA model's superior inpainting results across CelebA, Places2, and Paris StreetView datasets. Notably, the CSA model consistently achieved higher qualitative and quantitative measures, showcasing images with robust texture coherency and structural fidelity.

Technical Contributions

1. Coherent Semantic Attention Layer: The CSA layer ensures the semantic consistency of the generated content through an iterative process grounded in cross-correlation metrics between generated and contextual patches. This bi-directional mapping overcomes the drawbacks of existing spatial attention methods, enhancing feature continuity and mitigating boundary artifacts.
2. Consistency Loss: To stabilize the network training and guide more effective parameter learning, the authors incorporate a novel consistency loss. This measure targets alignment between VGG feature representations of ground-truth images and outputs from the CSA and decoder layers, thereby reinforcing the semantic alignment of inpainted images.
3. Feature Patch Discriminator: Integrated alongside the CSA layer, the feature patch discriminator refines the output by focusing on high-level discrepancies in feature space rather than traditional pixel-level discrepancies, contributing to the visual plausibility of inpainted regions.

Implications and Future Directions

This work sets a significant improvement in the domain of semantic inpainting by achieving more coherent and semantically aligned reconstructions of missing content in images. The implications of these advancements extend beyond inpainting; potential applications could unfold in related domains, such as style transfer and image synthesis for virtual reality and augmented reality environments.

The indication that future work could explore wider landscapes such as style transfer and single image super-resolution positions this research at the frontier of innovative image processing techniques. Such directions could potentially benefit from the robust feature continuity and texture coherence mechanisms presented in the CSA framework.

In summary, the coherent alignment strategies and semantic understanding encapsulated in this paper represent a significant advancement in inpainting technologies. The development of CSA demonstrates methodological enhancements that could inspire continued refinements and adoption in broader image processing fields, paving the way for future developments in AI-driven image analysis.