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Guided super-resolution is a unifying framework for several computer visiontasks where the inputs are a low-resolution source image of some targetquantity (e.g., perspective depth acquired with a time-of-flight camera) and ahigh-resolution guide image from a different domain (e.g., a grey-scale imagefrom a conventional camera); and the target output is a high-resolution versionof the source (in our example, a high-res depth map). The standard way oflooking 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 themissing high-frequency details from the guide. Here, we propose to turn thatinterpretation on its head and instead see it as a pixel-to-pixel mapping ofthe guide image to the domain of the source image. The pixel-wise mapping isparametrised as a multi-layer perceptron, whose weights are learned byminimising the discrepancies between the source image and the downsampledtarget image. Importantly, our formulation makes it possible to regularise onlythe mapping function, while avoiding regularisation of the outputs; thusproducing crisp, natural-looking images. The proposed method is unsupervised,using only the specific source and guide images to fit the mapping. We evaluateour method on two different tasks, super-resolution of depth maps and of treeheight maps. In both cases, we clearly outperform recent baselines inquantitative comparisons, while delivering visually much sharper outputs.

Guided Super-Resolution as Pixel-to-Pixel Transformation