RELLISUR: A Real Low-Light Image Super-Resolution Dataset

The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution.  The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf

RELLISUR数据集收录了真实低光照低分辨率图像，以及与之配对的正常光照高分辨率参考图像。本数据集旨在填补低光照图像增强与低分辨率图像增强（超分辨率，Super-Resolution, SR）领域间的研究空白——现有研究仅分别针对这两类问题开展，而现实场景中的图像往往同时受低光照与低分辨率双重因素影响，可视性普遍不佳。该数据集共包含12750组不同分辨率、不同低光照强度的配对图像，旨在支撑基于深度学习的模型训练，使模型能够直接将可视性较差的退化图像，映射为高质量且细节丰富的高分辨率图像。相关研究论文可通过以下链接获取：https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf