基于深度学习和人眼视觉系统的遥感图像质量评价 下载: 1643次
刘迪, 李迎春. 基于深度学习和人眼视觉系统的遥感图像质量评价[J]. 激光与光电子学进展, 2019, 56(6): 061101.
Di Liu, Yingchun Li. Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061101.
[1] 王俊琦, 张立国, 付天骄, 等. 基于骨架边缘提取的遥感图像清晰度评价方法[J]. 激光与光电子学进展, 2015, 52(9): 091002.
[2] Xia YT, Chen ZZ. Quality assessment for remote sensing images: Approaches and applications[C]∥IEEE International Conference on Systems, Man, and Cybernetics, October, 9-12, 2015, Kowloon, China. New York: IEEE, 2015: 1029- 1034.
[3] Sheikh HR, Bovik AC. A visual information fidelity approach to video quality assessment[C]∥International Workshop on Video Processing and Quality Metrics for Consumer Electronics, [S. l. : s. n.]2005: 23- 25.
[4] 苗莹, 易三莉, 贺建峰, 等. 结合梯度信息的特征相似性图像质量评估[J]. 中国图象图形学报, 2015, 20(6): 749-755.
[7] 邵宇, 孙富春, 李洪波. 基于视觉特性的无参考型遥感图像质量评价方法[J]. 清华大学学报(自然科学版), 2013, 53(4): 550-555.
Shao Y, Sun F C, Li H B. No-reference remote sensing image quality assessment method using visual properties[J]. Journal of Tsinghua University (Science and Technology), 2013, 53(4): 550-555.
[8] 张飞艳, 谢伟, 林立宇, 等. 基于小波域自然影像统计特性的无参考遥感影像质量评价[J]. 电子与信息学报, 2011, 33(11): 2742-2747.
[9] BareB, LiK, YanB, et al. An accurate deep convolutional neural networks model for no-reference image quality assessment[C]∥IEEE International Conference on Multimedia and Expo, July 10-14, 2017, Hong Kong, China. New York: IEEE, 2017: 1356- 1361.
[10] 李素梅, 常永莉, 段志成. 基于卷积神经网络的立体图像舒适度客观评价[J]. 光学学报, 2018, 38(6): 0610003.
[11] DengJ, DongW, SocherR, et al. ImageNet: A large-scale hierarchical image database[C]∥ IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 248- 255.
[13] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-13)[2018-08-06]. https:∥arxiv.org/abs/1409. 1556.
[14] YosinskiJ, CluneJ, BengioY, et al. How transferable are features in deep neural networks?[EB/OL]∥International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Canada,2014: 3320-3328.[2018-08-06]. https:∥arxiv.org/abs/1411. 1792.
[15] 高振宇, 杨晓梅, 龚剑明, 等. 图像复杂度描述方法研究[J]. 中国图象图形学报, 2010, 15(1): 129-135.
[16] 魏政刚, 袁杰辉, 蔡元龙. 图象质量评价方法的历史、现状和未来[J]. 中国图象图形学报: A辑, 1998, 3(5): 386-389.
刘迪, 李迎春. 基于深度学习和人眼视觉系统的遥感图像质量评价[J]. 激光与光电子学进展, 2019, 56(6): 061101. Di Liu, Yingchun Li. Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061101.