Next Article in Journal
A Semi-Supervised Model for Top-N Recommendation
Next Article in Special Issue
A Change Recommendation Approach Using Change Patterns of a Corresponding Test File
Previous Article in Journal
Lorentz-Violating Gravity Models and the Linearized Limit
Previous Article in Special Issue
A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(10), 491;

Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 29 September 2018 / Accepted: 11 October 2018 / Published: 12 October 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
Full-Text   |   PDF [3052 KB, uploaded 12 October 2018]   |  


Due to the cost limitation of camera sensors, images captured in low-light environments often suffer from low contrast and multiple types of noise. A number of algorithms have been proposed to improve contrast and suppress noise in the input low-light images. In this paper, a deep refinement network, LL-RefineNet, is built to learn from the synthetical dark and noisy training images, and perform image enhancement for natural low-light images in symmetric—forward and backward—pathways. The proposed network utilizes all the useful information from the down-sampling path to produce the high-resolution enhancement result, where global features captured from deeper layers are gradually refined using local features generated by earlier convolutions. We further design the training loss for mixed noise reduction. The experimental results show that the proposed LL-RefineNet outperforms the comparative methods both qualitatively and quantitatively with fast processing speed on both synthetic and natural low-light image datasets. View Full-Text
Keywords: low-light image; image enhancement; deep refinement network low-light image; image enhancement; deep refinement network

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Jiang, L.; Jing, Y.; Hu, S.; Ge, B.; Xiao, W. Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways. Symmetry 2018, 10, 491.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top