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

Andreas Aakerberg, Kamal Nasrollahi, Thomas B. Moeslund

Keywords: Low-light enhancement, super-resolution, image processing, image enhancement, image restoration

Abstract: In this paper, we introduce RELLISUR, a novel dataset of real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. With this dataset, we seek 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 are often limited by both low-light and low-resolution. Part of the reason for this, is the lack of a large-scale dataset. To this end, we release a dataset with 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 sharp and detail rich images of high resolution. Additionally, we provide a benchmark of the existing methods for separate Low Light Enhancement (LLE) and SR on the proposed dataset along with experiments with joint LLE and SR. The latter shows that joint processing results in more accurate reconstructions with better perceptual quality compared to sequential processing of the images. With this, we confirm that the new RELLISUR dataset can be useful for future machine learning research aimed at solving simultaneous image LLE and SR.

TL;DR: RELLISUR: A new large-scale dataset for joint low-light image enhancement and super-resolution.

URL: https://doi.org/10.5281/zenodo.5234969

Contribution Process Agreement: Yes

Dataset Url: https://doi.org/10.5281/zenodo.5234969

License: Creative Commons Attribution 4.0

Author Statement: Yes