Low-Light Image and Video Enhancement Using Deep Learning: A Survey

By Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Mingg-Ming Cheng, Jinwei Gu, Chen Change Loy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

573 citations recorded by the source corpus.

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have been employed. In this paper, we provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to unsolved open issues. To examine the generalization of existing methods, we propose a low-light image and video dataset, in which the images and videos are taken by different mobile phones’ cameras under diverse illumination conditions. Besides, for the first time, we provide a unified online platform that covers many popular LLIE methods, of which the results can be produced through a user-friendly web interface. In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark. This survey together with the proposed dataset and online platform could serve as a reference source for future study and promote the development of this research field. The proposed platform and dataset as well as the collected methods, datasets, and evaluation metrics are publicly available and will be regularly updated. Project page: https://www.mmlab-ntu.com/project/lliv_survey/index.html.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/22731d2a9fdb26684f04ad3864b21bf83dd5c03e

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

1. Tong Lu
2. Bo Dai
3. Jianmin Jiang
4. Wenhan Luo
5. Luc Van Gool
6. Chen Change Loy
7. Wenhan Yang
8. Pingping Zhang

Citations in the corpus

• Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, background): “LLIE aims to recover images from underexposed ones taken in low-light conditions (Li et al. 2022).”
• Generative Diffusion Prior for Unified Image Restoration and Enhancement (2023, methodology): “16 [41] Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, MingMing Cheng, Jinwei Gu, and Chen Change Loy.”
• Image De-Raining Transformer (2022, unknown): “…tasks [17], [18], [31], [32], [33], [34], [35], such as de-noising [36], [37], [38], super-resolution [39], [40], [41], de-blurring [42], [43], [44], [45], de-hazing [19], [19], [46], [47], [48], [49], [50], low-light image enhancement [51], [52], [53], [54], [55], style transfer [56], [57], etc .”
• URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement (2022, background): “Deep Learning-Based Methods: In the past decade, deep learning-based methods have provided promising results for LLIE problems [20].”