AsyncDSB: Schedule-Asynchronous Diffusion Schr\"odinger Bridge for Image Inpainting
Zihao Han, Baoquan Zhang, Lisai Zhang, Shanshan Feng, Kenghong Lin,, Guotao Liang, Yunming Ye, Xiaochen Qi, Guangming Ye

TL;DR
AsyncDSB introduces a novel asynchronous pixel scheduling strategy for diffusion Schr"odinger bridge-based image inpainting, addressing schedule-restoration mismatches and improving restoration quality by prioritizing high-frequency pixels.
Contribution
The paper proposes a schedule-asynchronous approach that better aligns the theoretical and practical processes in diffusion Schr"odinger bridge models for image inpainting.
Findings
Achieves 3%-14% FID improvement over baselines.
Effectively models pixel-wise asynchronous restoration.
Enhances image quality in real-world datasets.
Abstract
Image inpainting is an important image generation task, which aims to restore corrupted image from partial visible area. Recently, diffusion Schr\"odinger bridge methods effectively tackle this task by modeling the translation between corrupted and target images as a diffusion Schr\"odinger bridge process along a noising schedule path. Although these methods have shown superior performance, in this paper, we find that 1) existing methods suffer from a schedule-restoration mismatching issue, i.e., the theoretical schedule and practical restoration processes usually exist a large discrepancy, which theoretically results in the schedule not fully leveraged for restoring images; and 2) the key reason causing such issue is that the restoration process of all pixels are actually asynchronous but existing methods set a synchronous noise schedule to them, i.e., all pixels shares the same noise…
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Taxonomy
TopicsImage and Signal Denoising Methods · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
MethodsSparse Evolutionary Training · Diffusion · Inpainting
