1 |
刘金山. 基于深度神经网络的真实图像去噪的研究[D]. 西安: 西安电子科技大学, 2021.
|
|
LIU J S. Research on real-world image denoising based on deep neural network[D]. Xi'an: Xidian University, 2021. (in Chinese)
|
2 |
宋清昆, 马丽, 曹建坤, 等. 基于小波变换和均值滤波的图像去噪. 黑龙江大学自然科学学报, 2016, 33(4): 555- 560.
URL
|
|
SONG Q K, MA L, CAO J K, et al. Image denoising based on wavelet transform and mean filtering. Journal of Natural Sciences of Heilongjiang University, 2016, 33(4): 555- 560.
URL
|
3 |
张威. 空间域中均值滤波与中值滤波去噪的应用研究. 产业科技创新, 2020,(12): 65- 66.
|
|
ZHANG W. Application research on mean filtering and median filtering for denoising in spatial domain. Industrial Technological Innovation, 2020,(12): 65- 66.
|
4 |
ZHANG X B. Two-step non-local means method for image denoising. Multidimensional Systems and Signal Processing, 2022, 33(2): 341- 366.
doi: 10.1007/s11045-021-00802-y
|
5 |
司祯祯. 傅里叶变换与小波变换在信号去噪中的应用. 电子设计工程, 2011, 19(4): 155- 157.
URL
|
|
SI Z Z. Application of Fourier transform and wavelet transform in signal denoising. Electronic Design Engineering, 2011, 19(4): 155- 157.
URL
|
6 |
LEI Z F, SU W B, HU Q. Multimode decomposition and wavelet threshold denoising of mold level based on mutual information entropy. Entropy, 2019, 21(2): 1- 15.
|
7 |
ZHANG K, ZUO W M, CHEN Y J, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 2017, 26(7): 3142- 3155.
doi: 10.1109/TIP.2017.2662206
|
8 |
TAI Y, YANG J, LIU X M, et al. MemNet: a persistent memory network for image restoration[C]//Proceedings of International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2017: 4549-4557.
|
9 |
MARYAM G A, JAVAD A, PAUL B. Low-dose CT denoising with dilated residual network[C]//Proceedings of Annual International Conference on Engineering in Medicine and Biology Society. Washington D. C., USA: IEEE Press, 2018: 5117-5120.
|
10 |
SU Y M, LIAN Q S, ZHANG X H, et al. Multi-scale cross-path concatenation residual network for poisson denoising. IET Image Processing, 2019, 13(8): 1295- 1303.
doi: 10.1049/iet-ipr.2018.5941
|
11 |
ZHANG K, ZUO W M, ZHANG L. FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Transactions on Image Processing, 2018, 27(9): 4608- 4622.
doi: 10.1109/TIP.2018.2839891
|
12 |
KIM Y, SOH J W, PARK G Y, et al. Transfer learning from synthetic to real-noise denoising with adaptive instance normalization[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 3482-3492.
|
13 |
KIM D W, CHUNG J R, JUNG S W. GRDN: grouped residual dense network for real image denoising and GAN-based real-world noise modeling[EB/OL]. [2023-03-20]. https://arxiv.org/abs/1905.11172.
|
14 |
TIAN C W, XU Y, ZUO W M. Image denoising using deep CNN with batch renormalization. Neural Networks, 2020, 121, 461- 473.
doi: 10.1016/j.neunet.2019.08.022
|
15 |
HUI Z, GAO X B, YANG Y C, et al. Lightweight image super-resolution with information multi-distillation network[C]//Proceedings of the 27th International Conference on Multimedia. New York, USA: ACM Press, 2019: 2024-2032.
|
16 |
LIU J, TANG J, WU G S. Residual feature distillation network for lightweight image super-resolution[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 41-55.
|
17 |
HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(8): 2011- 2023.
|
18 |
ABDELHAMED A, LIN S, BROWN M S. A high-quality denoising dataset for smartphone cameras[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2018: 1692-1700.
|
19 |
|
20 |
ZAMIR S W, ARORA A, KHAN S, et al. Learning enriched features for fast image restoration and enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(2): 1934- 1948.
|
21 |
BURGER H C, SCHULER C J, HARMELING S. Image denoising: can plain neural networks compete with BM3D?[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2012: 2392-2399.
|
22 |
CHEN Y J, POCK T. Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1256- 1272.
doi: 10.1109/TPAMI.2016.2596743
|
23 |
DABOV K, FOI A, KATKOVNIK V, et al. Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space[C]//Proceedings of International Conference on Image Processing. Washington D. C., USA: IEEE Press, 2007: 313-316.
|
24 |
GU S H, ZHANG L, ZUO W M, et al. Weighted nuclear norm minimization with application to image denoising[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2014: 2862-2869.
|
25 |
|
26 |
ZORAN D, WEISS Y. From learning models of natural image patches to whole image restoration[C]//Proceedings of International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2011: 479-486.
|
27 |
GUO S, YAN Z F, ZHANG K, et al. Toward convolutional blind denoising of real photographs[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2019: 1712-1722.
|
28 |
ANWAR S, BARNES N. Real image denoising with feature attention[C]//Proceedings of International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 3155-3164.
|
29 |
YUE Z S, ZHAO Q, ZHANG L, et al. Dual adversarial network: toward real-world noise removal and noise generation[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 41-58.
|
30 |
YUE Z S, YONG H W, ZHAO Q, et al. Variational denoising network: toward blind noise modeling and removal[EB/OL]. [2023-03-20]. https://arxiv.org/abs/1908.11314.
|