Low-Level Dataset Distillation for Medical Image Enhancement

Medical image enhancement is clinically important, but ex-isting methods often rely on large-scale datasets to learn complex image-to-image mappings. Such reliance leads to high training and storage costsand hinders practical deployment.Dataset distillation (DD) provides a promising solution by synthesizingcompact datasets that preserve the training behavior of the original data.However, existing DD methods mainly focus on high-level tasks, wheremultiple samples share a common semantic label and the distilled datacan compress shared representations. In contrast, low-level medical im-age enhancement involves dense image-to-image mappings, where eachsample corresponds to a unique pixel-level target. This fundamental dif-ference makes low-level DD significantly more challenging and inherently