BluRef：基于稠密匹配参考的无监督图像去模糊
BluRef: Unsupervised Image Deblurring with Dense-Matching References
用稠密匹配模型在非配对模糊/清晰图之间建立像素对应，从而合成伪真值监督去模糊网络，为缺乏配对数据的复原任务提供了一条可复用的数据构造思路。
原文依据 uses unpaired blurred and sharp images of similar scenes to generate pseudo-ground truth data by leveraging a dense matching model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出一种新颖的无监督图像去模糊方法，其训练数据采集过程简单，从而提升了去模糊方法的适用性与有效性。该技术不需要精心配对的模糊图像与对应清晰图像；相反，它利用相似场景的非配对模糊图像与清晰图像，借助稠密匹配模型在模糊图像与参考清晰图像之间建立对应关系，从而生成伪真值数据。得益于训练数据采集过程的简便性，本方法不依赖现有的配对训练数据或预训练网络，因而对多种场景更具适应性，并适用于不同规模的网络，包括面向低资源设备设计的网络。我们证明这一新方法达到了最优性能，标志着图像去模糊领域的重要进展。
英文原文摘要 972 字符
This paper introduces a novel unsupervised approach for image deblurring that utilizes a simple process for training data collection, thereby enhancing the applicability and effectiveness of deblurring methods. Our technique does not require meticulously paired data of blurred and corresponding sharp images; instead, it uses unpaired blurred and sharp images of similar scenes to generate pseudo-ground truth data by leveraging a dense matching model to identify correspondences between a blurry image and reference sharp images. Thanks to the simplicity of the training data collection process, our approach does not rely on existing paired training data or pre-trained networks, making it more adaptable to various scenarios and suitable for networks of different sizes, including those designed for low-resource devices. We demonstrate that this novel approach achieves state-of-the-art performance, marking a significant advancement in the field of image deblurring.