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.

翻译：尽管近年来在图像运动模糊去除研究方面取得了进展，但处理强模糊仍然困难。虽然单张图像去模糊存在局限性，但利用多张图像（例如，使用额外图像作为参考来对模糊图像进行去模糊）具有更大潜力。一种典型设置是像视频去模糊研究中那样，利用视频序列中邻近的清晰图像对图像进行去模糊。本文提出了一种更好的方法来利用参考图像中的信息。该方法不需要对参考图像做出强假设。我们可以像视频去模糊一样，利用同一场景的替代镜头，甚至可以采用来自另一场景的不同图像。我们的方法首先匹配目标图像和参考图像的局部块，然后融合它们的特征以估计清晰图像。我们采用基于块的局部特征匹配策略来解决模糊图像与清晰参考图像匹配这一难题。我们的方法可以集成到为单图像去模糊设计的现有网络中。实验结果表明了所提方法的有效性。