利用短曝光图像指导去模糊，提升运动模糊区域的还原真实度。
ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring
- 结合上下文信息改进局部模糊检测精度
- 用短曝光图像中的清晰结构约束去模糊过程
- 基于扩散模型实现视觉更自然的图像重建，适合手机摄影
去除运动物体引起的模糊极具挑战，因运动部分严重模糊而静态背景清晰。现有依赖局部模糊检测的方法常因精度不足，仅聚焦模糊区域时难以生成满意结果。为此，我们设计了一种基于上下文的局部模糊检测模块，通过引入额外上下文信息提升模糊区域识别能力。考虑到现代智能手机具备短曝光成像能力，提出一种模糊感知的引导图像恢复方法，利用短曝光图像中的清晰结构信息，精准重建高度模糊区域。为进一步实现真实、美观的图像复原，开发了短曝光引导的扩散模型，从短曝光图像和模糊区域中提取有用特征，更好约束扩散过程。最终将上述组件整合为一个简单但高效的网络，命名为ExpRDiff。实验表明，ExpRDiff在性能上优于现有先进方法。
原文摘要 · Abstract (English)
Removing blur caused by moving objects is challenging, as the moving objects are usually significantly blurry while the static background remains clear. Existing methods that rely on local blur detection often suffer from inaccuracies and cannot generate satisfactory results when focusing solely on blurred regions. To overcome these problems, we first design a context-based local blur detection module that incorporates additional contextual information to improve the identification of blurry regions. Considering that modern smartphones are equipped with cameras capable of providing short-exposure images, we develop a blur-aware guided image restoration method that utilizes sharp structural details from short-exposure images, facilitating accurate reconstruction of heavily blurred regions. Furthermore, to restore images realistically and visually-pleasant, we develop a short-exposure guided diffusion model that explores useful features from short-exposure images and blurred regions to better constrain the diffusion process. Finally, we formulate the above components into a simple yet effective network, named ExpRDiff. Experimental results show that ExpRDiff performs favorably against state-of-the-art methods.
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