R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network

By Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang-Ju Lin, Songchen Han. Journal of Visual Communication and Image Representation, 2021

445 citations recorded by the source corpus.

Images captured in weak illumination conditions could seriously degrade the image quality. Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks. In this study, a novel Retinex-based Real-low to Real-normal Network (R2RNet) is proposed for low-light image enhancement, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net. These three subnets are used for decomposing, denoising, contrast enhancement and detail preservation, respectively. Our R2RNet not only uses the spatial information of the image to improve the contrast but also uses the frequency information to preserve the details. Therefore, our model acheived more robust results for all degraded images. Unlike most previous methods that were trained on synthetic images, we collected the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) to satisfy the training requirements and make our model have better generalization performance in real-world scenes. Extensive experiments on publicly available datasets demonstrated that our method outperforms the existing state-of-the-art methods both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions. Our codes and the LSRW dataset are available at: https://github.com/abcdef2000/R2RNet.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6901507cbaaa2fca80ac1ced76c40217a6850568

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Toward Fast, Flexible, and Robust Low-Light Image Enhancement (2022, unknown): “As for low-light image enhancement, we randomly sampled 100 images from MIT dataset [2] and 50 testing image from LSRW dataset [9] for testing.”
Low-Light Image Enhancement with Wavelet-Based Diffusion Models (2023, methodology): “We first compare our method with all comparison methods on the LOLv1 [Wei et al. 2018], LOLv2-real [Yang et al. 2021b], and LSRW [Hai et al. 2023] test sets.”