LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement

By Chongyi Li, Jichang Guo, Fatih Murat Porikli, Yanwei Pang. Pattern Recognition Letters, 2018

484 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c6963e6bf8e60562f421ad535aaedd2251264dde

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. Chongyi Li
2. Chunle Guo
3. Runmin Cong
4. Sam Kwong
5. Junhui Hou
6. Chen Change Loy
7. Xiaohui Yuan
8. Jichang Guo

Citations in the corpus

• Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, methodology): “CNN-based models usually use self-captured paired data for network training [5, 17, 28, 30, 32, 33] while GAN-based models elaborately select unpaired data [6,11,12,16,35].”
• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “designed a network called LightenNet based on a CNN architecture [271]; this network takes a weakly illuminated image as input and outputs a corresponding illumination map, which is subsequently used to generate an enhanced image based on the Retinex model.”