TY - JOUR
T1 - LEGAN: A low-light image enhancement generative adversarial network for industrial internet of smart-cameras
AU - Tao, Jing
AU - Wang, Junliang
AU - Zhang, Peng
AU - Zhang, Jie
AU - Yung, Kai Leung
AU - Ip, Wai Hung
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China-General Program (Grant Nos. 52275478), Young Elite Scientists Sponsorship Program by CAST, China (Grant Nos. 2021QNRC001) and Xinjiang key research and development program, China (Grant Nos. 2022B01057-1).
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/4
Y1 - 2024/4
N2 - The utilization of smart-cameras in the context of the Internet of Things (IoT) has become increasingly prevalent within smart workshops for performing in-situ quality inspection tasks. However, it is worth noting that these smart-cameras may encounter operational challenges when functioning under low-light conditions. The images acquired in such situation are severely degraded, resulting in the performance decline of the subsequent detection algorithms. Focusing on non-stationary noise compression and detail recovery, this paper constructs a novel enhancement model called LEGAN for the industrial internet of smart-cameras system. Firstly, the input undergoes a decomposition process into two branches using the Harr-wavelet technique. These branches are subsequently encoded independently by a series of compact residual blocks, facilitating effective noise suppression. Secondly, in order to enhance detail recovery, a feature selection module is meticulously designed to extract correlations between image foreground–background and low–high frequency signals, ultimately reconstructing a comprehensive feature map. This enables a multi-scale stepwise up-sampling approach that facilitates image recovery based on the reconstructed feature maps. Lastly, the training phase is supervised by an adversarial loss, comprising MSE loss, VGG loss, and discriminating loss, which ensures a harmonious balance between noise suppression and detail recovery. Comparative experiments clearly show the superiority of the LEGAN in terms of noise compression and detail recovery. Moreover, from an industrial practice perspective, the application of the proposed approach to yarn evenness inspection has proven to be highly effective, significantly enhancing detection accuracy in low-light environments.
AB - The utilization of smart-cameras in the context of the Internet of Things (IoT) has become increasingly prevalent within smart workshops for performing in-situ quality inspection tasks. However, it is worth noting that these smart-cameras may encounter operational challenges when functioning under low-light conditions. The images acquired in such situation are severely degraded, resulting in the performance decline of the subsequent detection algorithms. Focusing on non-stationary noise compression and detail recovery, this paper constructs a novel enhancement model called LEGAN for the industrial internet of smart-cameras system. Firstly, the input undergoes a decomposition process into two branches using the Harr-wavelet technique. These branches are subsequently encoded independently by a series of compact residual blocks, facilitating effective noise suppression. Secondly, in order to enhance detail recovery, a feature selection module is meticulously designed to extract correlations between image foreground–background and low–high frequency signals, ultimately reconstructing a comprehensive feature map. This enables a multi-scale stepwise up-sampling approach that facilitates image recovery based on the reconstructed feature maps. Lastly, the training phase is supervised by an adversarial loss, comprising MSE loss, VGG loss, and discriminating loss, which ensures a harmonious balance between noise suppression and detail recovery. Comparative experiments clearly show the superiority of the LEGAN in terms of noise compression and detail recovery. Moreover, from an industrial practice perspective, the application of the proposed approach to yarn evenness inspection has proven to be highly effective, significantly enhancing detection accuracy in low-light environments.
KW - Internet of smart-cameras
KW - Quality detection
KW - Low-light image enhancement
KW - GAN
KW - Independent encoding
KW - Feature selection
UR - http://www.scopus.com/inward/record.url?scp=85181806418&partnerID=8YFLogxK
U2 - 10.1016/j.iot.2023.101054
DO - 10.1016/j.iot.2023.101054
M3 - Journal article
SN - 2542-6605
VL - 25
JO - Internet of Things (Netherlands)
JF - Internet of Things (Netherlands)
M1 - 101054
ER -