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Image Super-Resolution
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
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84개Celeb-HQ 4x upscalingChikusei DatasetDIV2K val - 16x upscalingDIV8K test - 16x upscalingEPFL NIR-VISGeneral-100 - 4x upscalingKITTI 2012 - 2x upscalingKITTI 2012 - 4x upscalingKITTI 2015 - 2x upscalingKITTI 2015 - 4x upscalingKITTI2012 - 2x scalingManga109 - 16x upscalingMiddlebury - 2x upscalingMiddlebury - 4x upscalingOLI2MSI - 3x upscalingSen2venus - 4x upscalingSet14Set5 - 5x upscalingSet5 - 6x upscalingShipSpottingTextZoomUSR-248 - 4x upscalingUrban100 - 16x upscalingWLFW