Despite the recent advancement in the study of removing motion blur in an
image, it is still hard to deal with strong blurs. While there are limits in
removing blurs from a single image, it has more potential to use multiple
images, e.g., using an additional image as a reference to deblur a blurry
image. A typical setting is deburring an image using a nearby sharp image(s) in
a video sequence, as in the studies of video deblurring. This paper proposes a
better method to use the information present in a reference image. The method
does not need a strong assumption on the reference image. We can utilize an
alternative shot of the identical scene, just like in video deblurring, or we
can even employ a distinct image from another scene. Our method first matches
local patches of the target and reference images and then fuses their features
to estimate a sharp image. We employ a patch-based feature matching strategy to
solve the difficult problem of matching the blurry image with the sharp
reference. Our method can be integrated into pre-existing networks designed for
single image deblurring. The experimental results show the effectiveness of the
proposed method.

使用多个图像进行去模糊处理的新方法，通过匹配目标图像和参考图像的局部块并融合其特征来估计清晰图像，实验证明了该方法的有效性。

基于参考图的运动模糊去除：学习如何利用参考图像的锐度

Reference-based Motion Blur Removal: Learning to Utilize Sharpness in  the Reference Image

We present a novel approach to reference-based super-resolution (RefSR) with
the focus on dual-camera super-resolution (DCSR), which utilizes reference
images for high-quality and high-fidelity results. Our proposed method
generalizes the standard patch-based feature matching with spatial alignment
operations. We further explore the dual-camera super-resolution that is one
promising application of RefSR, and build a dataset that consists of 146 image
pairs from the main and telephoto cameras in a smartphone. To bridge the domain
gaps between real-world images and the training images, we propose a
self-supervised domain adaptation strategy for real-world images. Extensive
experiments on our dataset and a public benchmark demonstrate clear improvement
achieved by our method over state of the art in both quantitative evaluation
and visual comparisons.

该研究提出了一种使用参考图像进行高保真图像超分辨率的新方法，针对双摄像头超分辨率进行了重点研究，其利用空间对齐操作推广了标准的基于补丁的特征匹配方法。进一步探索了 RefSR 的一种有前途的应用：双摄像头超分辨率，并构建了由智能手机主摄像头和长焦摄像头的 146 个图像对组成的数据集。此外，为了弥合现实世界图像和训练图像之间的领域差距，该研究提出了一种自我监督的领域自适应策略。对数据集和公共基准的广泛实验表明，该方法在定量评估和视觉比较方面均显著优于现有最优方法。