Guided super-resolution is a unifying framework for several computer vision
tasks where the inputs are a low-resolution source image of some target
quantity (e.g., perspective depth acquired with a time-of-flight camera) and a
high-resolution guide image from a different domain (e.g., a grey-scale image
from a conventional camera); and the target output is a high-resolution version
of the source (in our example, a high-res depth map). The standard way of
looking at this problem is to formulate it as a super-resolution task, i.e.,
the source image is upsampled to the target resolution, while transferring the
missing high-frequency details from the guide. Here, we propose to turn that
interpretation on its head and instead see it as a pixel-to-pixel mapping of
the guide image to the domain of the source image. The pixel-wise mapping is
parametrised as a multi-layer perceptron, whose weights are learned by
minimising the discrepancies between the source image and the downsampled
target image. Importantly, our formulation makes it possible to regularise only
the mapping function, while avoiding regularisation of the outputs; thus
producing crisp, natural-looking images. The proposed method is unsupervised,
using only the specific source and guide images to fit the mapping. We evaluate
our method on two different tasks, super-resolution of depth maps and of tree
height maps. In both cases, we clearly outperform recent baselines in
quantitative comparisons, while delivering visually much sharper outputs.

该研究提出了一种新颖的向导超分辨率方法，将问题视为像素级向导图到源图像域的映射，使用无监督学习和多层感知机来参数化映射函数，达到超分辨率的效果，并在深度图和树高图两个任务中均优于其他基线算法。