One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block
Hongyu Liu, Bin Jiang, Wei Huang, Chao Yang

TL;DR
This paper introduces a novel single-network deep learning method for image inpainting that employs Pyramid Filling Blocks and Bilateral Attention layers to produce high-quality, structurally coherent results more efficiently than traditional two-stage models.
Contribution
The paper proposes a new single-network inpainting approach with Pyramid Filling Blocks and Bilateral Attention, reducing inference time and improving result quality over existing two-stage methods.
Findings
Outperforms existing methods on multiple datasets
Produces sharper and more structurally coherent inpainted images
Reduces inference time compared to two-stage architectures
Abstract
Recent deep learning based image inpainting methods which utilize contextual information and two-stage architecture have exhibited remarkable performance. However, the two-stage architecture is time-consuming, the contextual information lack high-level semantics and ignores both the semantic relevance and distance information of hole's feature patches, these limitations result in blurry textures and distorted structures of final result. Motivated by these observations, we propose a new deep generative model-based approach, which trains a shared network twice with different targets and utilizes a single network during the testing phase, so that we can effectively save inference time. Specifically, the targets of two training steps are structure reconstruction and texture generation respectively. During the second training, we first propose a Pyramid Filling Block (PF-block) to utilize…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Advanced Image Processing Techniques
