Infrared and Laser Engineering, Volume. 51, Issue 8, 20210957(2022)
Parallel multifeature extracting network for infrared image enhancement
[1] Zuo C, Yang X, Zhang J, et al. Super-resolution enhancement of infrared images using a lightweight dense residual network[J]. Infrared Technology, 43, 251-257(2021).
[2] Li P, Liu Y, Xu A. Infrared image enhancement using dense residual network with multi-scale coupling[J]. Journal of Electronic Measurement and Instrumentation, 35, 148-155(2021).
[3] [3] Choi Y, Kim N, Hwang S, et al. Thermal image enhancement using convolutional neural wk[C]2016 IEEERSJ International Conference on Intelligent Robots Systems (IROS). IEEE, 2016.
[4] Wang D, Shen T, Sun B, et al. Infrared image enhancement algorithm based on atmospheric gray factor[J]. Laser and Infrared, 49, 1135-1140(2019).
[5] Li M, Zhou R, Tian Z. A thermal infrared image enhancement method based on histogram[J]. Infrared Technology, 42, 880-885(2020).
[6] Li J, Li S, Duan X, et al. Infrared image enhancement based on retinex and probability nonlocal means filtering[J]. Acta Photonica Sinica, 49, 0410003(2020).
[7] Cao H, Liu N, Xu J, et al. Infrared image adaptive inverse histogram enhancement technology[J]. Infrared and Laser Engineering, 49, 0426003(2020).
[8] Li S, Jin W, Li L, et al. An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization[J]. Infrared Physics & Technology, 90, 164-174(2018).
[9] [9] Liang X, Tian Y, Yan S, et al. A realtime infrared image enhancement algithm based on improved CLAHE[C]2018 International Conference on Image Video Processing, Artificial Intelligence, 2018: 10836.
[10] Lee K, Lee J, Lee J, et al. Brightness-based convolutional neural network for thermal image enhancement[J]. IEEE Access, 5, 26867-26879(2017).
[11] Kuang X, Sui X, Liu Y, et al. Single infrared image enhancement using a deep convolutional neural network[J]. Neurocomputing, 332, 119-128(2019).
[12] He Z, Tang S, Yang J, et al. Cascaded deep networks with multiple receptive fields for infrared image super-resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 2310-2322(2019).
[13] Wang X J, Ouyang W S. Multi-scale recurrent attention network for image motion deblurring[J]. Infrared and Laser Engineering, 51, 20210605(2022).
[14] Tian C, Xu Y, Zuo W. Image denoising using deep CNN with batch renormalization[J]. Neural Networks, 121, 461-473(2020).
[15] [15] Deng J, Dong W, Socher R, et al. Image: A largescale hierarchical image database[C]2009 IEEE Conference on Computer Vision Pattern Recognition. IEEE, 2009.
[16] [16] Martin D, Fowlkes C, Tal D, et al. A database of human segmented natural images its application to evaluating segmentation algithms measuring ecological statistics[C]Proceedings Eighth IEEE International Conference on Computer Vision. ICCV. IEEE, 2001, 2: 416423.
[17] [17] Toet A. TNO image fusion dataset. figshare[DBOL]. (2014)[20211213]. https:doi.g10.6084m9.figshare.1008029.v1.
[18] [18] Davis J W, Keck M A. A twostage template approach to person detection in thermal imagery[C]2005 Seventh IEEE Wkshops on Applications of Computer Vision (WACVMOTION''05). IEEE, 2005, 1: 364369.
Get Citation
Copy Citation Text
Zhongxiang Pang, Xie Liu, Guihua Liu, Yinjun Gong, Han Zhou, Hongwei Luo. Parallel multifeature extracting network for infrared image enhancement[J]. Infrared and Laser Engineering, 2022, 51(8): 20210957
Category: Infrared technology and application
Received: Dec. 13, 2021
Accepted: --
Published Online: Jan. 9, 2023
The Author Email: