用手机激光雷达深度图实现真实模糊图像去模糊，无需配对训练数据。
A novel method and dataset for depth-guided image deblurring from smartphone Lidar
- 基于去噪扩散模型，利用激光雷达深度图引导去模糊。
- 在自建真实数据集上，感知质量优于现有最先进方法。
- 适合研究移动设备多传感器融合与无监督图像恢复的人群。
现代智能手机配备激光雷达传感器，具备深度感知能力。已有研究证明该互补传感器可提升图像处理任务性能，包括去模糊。然而，目前缺乏包含真实模糊图像及对应移动端激光雷达深度图的数据集，也缺少无需大量成对数据训练的盲零样本去模糊方法。本文提出一种基于去噪扩散模型的图像去模糊方法，可利用激光雷达深度图进行引导，且不需依赖成对深度图的训练数据。同时，我们发布了首个使用苹果iPhone 15 Pro采集的真实模糊图像、对应激光雷达深度图及清晰真实图像的数据集，用于研究激光雷达引导的去模糊。在该新数据集上的实验表明，激光雷达引导有效，所提方法在感知质量上优于现有最先进方法。
原文摘要 · Abstract (English)
Modern smartphones are equipped with Lidar sensors providing depth-sensing capabilities. Recent works have shown that this complementary sensor allows to improve various tasks in image processing, including deblurring. However, there is a current lack of datasets with realistic blurred images and paired mobile Lidar depth maps to further study the topic. At the same time, there is also a lack of blind zero-shot methods that can deblur a real image using the depth guidance without requiring extensive training sets of paired data. In this paper, we propose an image deblurring method based on denoising diffusion models that can leverage the Lidar depth guidance and does not require training data with paired Lidar depth maps. We also present the first dataset with real blurred images with corresponding Lidar depth maps and sharp ground truth images, acquired with an Apple iPhone 15 Pro, for the purpose of studying Lidar-guided deblurring. Experimental results on this novel dataset show that Lidar guidance is effective and the proposed method outperforms state-of-the-art deblurring methods in terms of perceptual quality.
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