[1]
|
Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612. |
[2]
|
Wang Z, Li Q. Information content weighting for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(5):1185-1198. |
[3]
|
Sheikh H R, Bovik A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 2006, 15(2):430-444. |
[4]
|
Cheng G, Huang J C, Zhu C, et al. Perceptual image quality assessment using a geometric structural distortion model[C]//IEEE International Conference on Image Processing, 2010:325-328. |
[5]
|
Zhang D. FSIM:A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8):2378-2386. |
[6]
|
Xue W, Zhang L, Mou X, et al. Gradient magnitude similarity deviation:A highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23(2):684-695. |
[7]
|
Luo Haibo, He Miao, Hui Bin, et al. Pedestrian detection algorithm based on dual-model fused fully convolutional networks[J]. Infrared and Laser Engineering, 2018, 47(2):0203001. (in Chinese)罗海波, 何淼, 惠斌,等. 基于双模全卷积网络的行人检测算法(特邀)[J]. 红外与激光工程, 2018, 47(2):0203001. |
[8]
|
Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5):0502002. (in Chinese)罗海波, 许凌云, 惠斌,等. 基于深度学习的目标跟踪方法研究现状与展望[J]. 红外与激光工程, 2017, 46(5):0502002. |
[9]
|
Kang L, Ye P, Li Y, et al. Convolutional neural networks for No-reference image quality assessment[C]//Computer Vision and Pattern Recognition, IEEE, 2014:1733-1740. |
[10]
|
Li Y, Po L M, Feng L, et al. No-reference image quality assessment with deep convolutional neural networks[C]//IEEE International Conference on Digital Signal Processing, 2017:685-689. |
[11]
|
Kim J, Lee S. Fully deep blind image quality predictor[J]. IEEE Journal of Selected Topics in Signal Processing, 2017, 11(1):206-220. |
[12]
|
Ali Amirshahi S, Pedersen M, Yu S X. Image quality assessment by comparing CNN features between images[J]. Electronic Imaging, 2016, 60(6):6041010. |
[13]
|
Gao F, Wang Y, Li P, et al. Deep Sim:Deep similarity for image quality assessment[J]. Neurocomputing, 2017(1):104-114. |
[14]
|
Mahendran A, Vedaldi A. Visualizing deep convolutional neural Networks using natural pre-images[J]. International Journal of Computer Vision, 2016, 120(4):1-23. |
[15]
|
Ponomarenko N, Lukin V, Zelensky A, et al. TID2008-a database for evaluation of full-reference visual quality assessment metrics[J]. Adv Modern Radioelectron, 2009, 10(1):30-45. |