TY - GEN
T1 - An effective network with convLSTM for low-light image enhancement
AU - Xiang, Yixi
AU - Fu, Ying
AU - Zhang, Lei
AU - Huang, Hua
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Low-light image enhancement is a fundamental problem in computer vision. The artifact, noise, insufficient contrast and color distortion are common challenging problems in low-light image enhancement. In this paper, we present a convolutional Long Short-Term Memory (ConvLSTM) network based method to directly restore a normal image from a low-light image, which can be learned in an end-to-end way. Specifically, our base network employs the encoder-decoder structure. Meanwhile, considering that a normal image may correspond to low-light images of different illuminance levels, we adopt a multi-branch structure combined with ConvLSTM to solve this problem. The extensive experiments on two low-light datasets show that our method outperforms the state-of-the-art traditional and deep learning based methods vertified by both quantitative and qualitative evaluation.
AB - Low-light image enhancement is a fundamental problem in computer vision. The artifact, noise, insufficient contrast and color distortion are common challenging problems in low-light image enhancement. In this paper, we present a convolutional Long Short-Term Memory (ConvLSTM) network based method to directly restore a normal image from a low-light image, which can be learned in an end-to-end way. Specifically, our base network employs the encoder-decoder structure. Meanwhile, considering that a normal image may correspond to low-light images of different illuminance levels, we adopt a multi-branch structure combined with ConvLSTM to solve this problem. The extensive experiments on two low-light datasets show that our method outperforms the state-of-the-art traditional and deep learning based methods vertified by both quantitative and qualitative evaluation.
KW - ConvLSTM
KW - Low-light image enhancement
UR - http://www.scopus.com/inward/record.url?scp=85076974445&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31723-2_19
DO - 10.1007/978-3-030-31723-2_19
M3 - Conference contribution
AN - SCOPUS:85076974445
SN - 9783030317225
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 233
BT - Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II
A2 - Lin, Zhouchen
A2 - Wang, Liang
A2 - Tan, Tieniu
A2 - Yang, Jian
A2 - Shi, Guangming
A2 - Zheng, Nanning
A2 - Chen, Xilin
A2 - Zhang, Yanning
PB - Springer
T2 - 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
Y2 - 8 November 2019 through 11 November 2019
ER -