[1] Chen M J. Research on nuclear radiation contaminated image enhancement based on total variation and sparsity representation[D](2020).
[2] Xu W F, Mao Z G. Research status and development trend of nuclear power plant robots[J]. Robot, 33, 758-767(2011).
[3] Tsitsimpelis I, Taylor C J, Lennox B et al. A review of ground-based robotic systems for the characterization of nuclear environments[J]. Progress in Nuclear Energy, 111, 109-124(2019).
[4] Ni J L, Chen P, Li S C et al. AP1000 radiation monitoring system design and engineering solution[J]. Nuclear Electronics & Detection Technology, 33, 1405-1407, 1426(2013).
[5] Torii T, Sanada Y, Nishizawa Y et al. Radiation monitoring using an unmanned helicopter in the evacuation zone set up by the fukushima daiichi NPP accident[EB/OL]. http://www.meetingorganizer.copernicus.org/EGU2013/EGU2013-3794.pdf
[6] Cai Y L, Li Y D, Wen L et al. Progress of single event effects and hardening technology of CMOS image sensors[J]. Nuclear Techniques, 43, 50-58(2020).
[7] Li L N, Sun R J, Chen M Y et al. Research progress on radiation protection materials[J]. Synthetic Fiber in China, 48, 21-25(2019).
[8] Buyuk B. Preparation and characterization of iron-ore-imbedded silicone rubber materials for radiation protection[J]. Nuclear Science and Techniques, 29, 135(2018).
[9] Wang H, Sang R J, Zhang H et al. A new image denoising method for monitoring in intense radioactive environment[J]. Transducer and Microsystem Technologies, 30, 59-61(2011).
[10] Yang B, Zhao L H, Deng Q. A novel anti-nuclear radiation image restoration algorithm based on inpainting technology[J]. Journal of University of South China (Science and Technology), 30, 56-61(2016).
[11] Hosoya N, Miyamoto A, Naganuma J. Real-time image improvement system for visual testing of nuclear reactors[C], 17047598(2017).
[12] Li Y Y, Jin W Q, Liu Z H. Interior radiation noise reduction method based on multiframe processing in infrared focal plane arrays imaging system[J]. IEEE Photonics Journal, 10, 18051775(2018).
[13] Zhu X F, Jing L, Shao D G. Ultrasonic image denoising using adaptive bilateral filtering based on back propagation neural network[J]. Laser & Optoelectronics Progress, 57, 241014(2020).
[14] Wu Q, Zhang R. Wavelet denoising of near-earth all-day star map based on local outlier factor[J]. Acta Optica Sinica, 40, 0810001(2020).
[15] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 629-639(1990).
[16] Xu X Z, Zhao W C, Xu F Q et al. Improved speckle reducing anisotropic diffusion for ultrasound image filtering[J]. Optics and Precision Engineering, 25, 1662-1668(2017).
[17] Guo F C, Zhang G, Zhang Q J et al. Fusion despeckling based on surface variation anisotropic diffusion filter and ratio image filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 2398-2411(2020).
[18] Yilmaz E, Kayikcioglu T, Kayipmaz S. Noise removal of CBCT images using an adaptive anisotropic diffusion filter[C], 650-653(2017).
[19] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).
[20] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C], 60-65(2005).
[21] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 60, 259-268(1992).
[22] He K M, Sun J, Tang X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1397-1409(2013).
[23] Tian C W, Fei L K, Zheng W X et al. Deep learning on image denoising: an overview[J]. Neural Networks, 131, 251-275(2020).
[24] Lehtinen J, Munkberg J, Hasselgren J et al. Noise2Noise: learning image restoration without clean data[EB/OL]. https://arxiv.org/abs/1803.04189