激光与光电子学进展, 2019, 56 (6): 061101, 网络出版: 2019-07-30   

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

Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System
作者单位
航天工程大学电子与光学工程系, 北京 101416
引用该论文

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.

参考文献

[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.

    Wang J Q, Zhang L G, Fu T J, et al. Sharpness assessment for remote sensing image based on abstracting the edge image of skeleton[J]. Laser & Optoelectronics Progress, 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.

    Miao Y, Yi S L, He J F, et al. Image quality assessment of feature similarity combined with gradient information[J]. Journal of Image and Graphics, 2015, 20(6): 749-755.

[5] Chandler D M, Hemami S S. VSNR: A wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2284-2298.

[6] Kim J, Zeng H, Ghadiyaram D, et al. Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment[J]. IEEE Signal Processing Magazine, 2017, 34(6): 130-141.

[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.

    Zhang F Y, Xie W, Lin L Y, et al. No-reference remote sensing image quality assessment based on natural scene statistical in wavelet domain[J]. Journal of Electronics & Information Technology, 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.

    Li S M, Chang Y L, Duan Z C. Objective assessment of stereoscopic image comfort based on convolutional neural network[J]. Acta Optica Sinica, 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.

[12] Yu W, Yang K Y, Yao H X, et al. Exploiting the complementary strengths of multi-layer CNN features for image retrieval[J]. Neurocomputing, 2017, 237: 235-241.

[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.

    Gao Z N, Yang X M, Gong J M, et al. Research on image complexity description methods[J]. Journal of Image and Graphics, 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.

    Wei Z G, Yuan J H, Cai Y L. The history, status and future of image quality evaluation[J]. Journal of Image and Graphics: Edition A, 1998, 3(5): 386-389.

[17] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3440-3451.

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.

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