In this paper we address the problem of matching two images with two different resolutions: a high-resolution image and a low-resolution one. The difference in resolution between the two images is not known and without loss of generality one of the images is assumed to be the high-resolution one. On the premise that changes in resolution act as a smoothing equivalent to changes in scale, a scale-space representation of the high-resolution image is produced. Hence the one-to-one classical image matching paradigm becomes one-to-many because the low-resolution image is compared with all the scale-space representations of the high-resolution one. Key to the success of such a process is the proper representation of the features to be matched in scale-space. We show how to represent and extract interest points at variable scales and we devise a method allowing the comparison of two images at two different resolutions. The method comprises the use of photometric- and rotation-invariant descriptors, a geometric model mapping the high-resolution image onto a low-resolution image region, and an image matching strategy based on local constraints and on the robust estimation of this geometric model. Extensive experiments show that our matching method can be used for scale changes up to a factor of 6.

翻译：本文研究了分辨率不同的两幅图像（高分辨率图像与低分辨率图像）的匹配问题。两幅图像的分辨率差异未知，且不失一般性地假设其中一幅为高分辨率图像。基于分辨率变化相当于尺度变化所对应的平滑处理的假设，本文构建了高分辨率图像的尺度空间表示。由此，经典的一对一图像匹配范式转化为一对多匹配——低分辨率图像需与所有高分辨率图像的尺度空间表示进行比较。该过程成功的关键在于合理表示尺度空间中待匹配的特征。本文阐述了如何表示并提取变尺度下的兴趣点，并设计了一种比较两幅不同分辨率图像的方法。该方法包含以下步骤：采用光照与旋转不变描述子、构建将高分辨率图像映射至低分辨率图像区域的几何模型、以及基于局部约束和该几何模型鲁棒估计的图像匹配策略。大量实验证明，我们的匹配方法可处理高达6倍的尺度变化。