用扩散模型从模糊图像中恢复高分辨率运动轨迹
MoTDiff: High-resolution Motion Trajectory estimation from a single blurred image using Diffusion models
- 基于多尺度特征的条件扩散框架，从单张模糊图推断运动轨迹
- 在去模糊和编码曝光任务中显著优于现有方法
- 适合需要精确运动信息的视觉重建与动态感知场景
准确估计运动信息在计算成像与计算机视觉中至关重要。以往方法多从单张模糊图像中提取模糊核或光流，但现有运动表示通常质量较低，表现为粗粒度且不准确。本文提出首个基于扩散模型的高分辨率（HR）运动轨迹估计框架MoTDiff。不同于传统表示，该方法旨在从单张运动模糊图像中恢复高质量、高分辨率的运动轨迹。MoTDiff包含两个核心组件：1）利用从单张模糊图像提取的多尺度特征图作为条件的新型条件扩散框架；2）一种新训练策略，可促进精细运动轨迹的精准识别、运动路径整体形状与位置的一致估计，以及轨迹像素间的连通性保持。实验表明，该方法在盲图像去模糊与编码曝光摄影任务中均超越当前最优水平。
原文摘要 · Abstract (English)
Accurate estimation of motion information is crucial in diverse computational imaging and computer vision applications. Researchers have investigated various methods to extract motion information from a single blurred image, including blur kernels and optical flow. However, existing motion representations are often of low quality, i.e., coarse-grained and inaccurate. In this paper, we propose the first high-resolution (HR) Motion Trajectory estimation framework using Diffusion models (MoTDiff). Different from existing motion representations, we aim to estimate an HR motion trajectory with high-quality from a single motion-blurred image. The proposed MoTDiff consists of two key components: 1) a new conditional diffusion framework that uses multi-scale feature maps extracted from a single blurred image as a condition, and 2) a new training method that can promote precise identification of a fine-grained motion trajectory, consistent estimation of overall shape and position of a motion path, and pixel connectivity along a motion trajectory. Our experiments demonstrate that the proposed MoTDiff can outperform state-of-the-art methods in both blind image deblurring and coded exposure photography applications.
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