[12] Barash D. Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(6): 844-847.

[13] Spira A, Kimmel R, Sochen N. A short-time Beltrami kernel for smoothing images and manifolds[J]. IEEE Transactions on Image Processing, 2007, 16(6): 1628-1636.

[14] Kervrann C, Boulanger J. Optimal spatial adaptation for patch-based image denoising[J]. IEEE Transactions on Image Processing, 2006, 15(10): 2866-2878.

[18] ZHANG X, LI J, XING J, et al. A particle swarm optimization technique-based parametric wavelet thresholding function for signal denoising[J]. Circuits, Systems, and Signal Processing, 2017, 36: 247-269.

[20] Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.

[21] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.

[24] HEO Y C, KIM K, LEE Y. Image denoising using non-local means (NLM) approach in magnetic resonance (MR) imaging: a systematic review[J]. Applied Sciences, 2020, 10(20): 7028.

[29] DU D, PAN Z, ZHANG P, et al. Compressive sensing image recovery using dictionary learning and shape-adaptive DCT thresholding[J]. Magnetic Resonance Imaging, 2019, 55: 60-71.

[30] LIU B, LIU J. Overview of image denoising based on deep learning[J]. Journal of Physics: Conference Series, 2019, 1176(2): 022010.

[33] Kanika G. Study of Deep Learning Techniques on Image Denoising[C]//IOP Conference Series: Materials Science and Engineering, 2021, 1022(1): 012007.

[34] Ilesanmi E A, Ilesanmi O T. Methods for image denoising using convolutional neural network: a review[J]. Complex & Intelligent Systems, 2021, 7(5): 1-20.

[35] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[36] Krizhevsky Alex, Ilya Sutskever, Geoffrey E Hinton. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[37] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. arXiv: 1409.1556, https://arxiv.org/abs/1409.1556.

[38] Szegedy Christian, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[J]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 1-9.

[39] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

[40] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv: 1511.06434, 2015.

[41] TIAN C, FEI L, ZHENG W, et al. Deep learning on image denoising: An overview[J]. Neural Networks, 2020, 131: 251-275.

[42] XU Q, ZHANG C, ZHANG L. Denoising convolutional neural network[C]//2015 IEEE International Conference on Information and Automation, IEEE, 2015: 1184-1187.

[43] LIANG J, LIU R. Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network[C]//2015 8th International Congress on Image and Signal Processing (CISP). IEEE, 2015: 697-701.

[44] CHENG W, LIN L. Application of improved neural network algorithm in image denoising and edge detection[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016, 9(6): 269-282.

[45] ZHANG K, ZUO W, GU S, et al. Learning deep CNN denoiser prior for image restoration[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3929-3938.

[46] KUANG X, SUI X, LIU Y, et al. Single infrared image optical noise removal using a deep convolutional neural network[J]. IEEE Photonics Journal, 2017, 10(2): 1-15.

[47] ZHANG K, ZUO W, ZHANG L. FFDNet: Toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4608-4622.

[48] HAN Y, YE J C. Framing U-Net via deep convolutional framelets: Application to sparse-view CT[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1418-1429.

[49] HE Z, CAO Y, DONG Y, et al. Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: A deep-learning approach[J]. Applied Optics, 2018, 57(18): D155-D164.

[50] YAN H, CHEN X, TAN V Y F, et al. Unsupervised image noise modeling with self-consistent GAN[J]. arXiv preprint arXiv: 1906.05762, 2019.

[51] ZHANG K, ZUO W, ZHANG L. Deep plug-and-play super-resolution for arbitrary blur kernels[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 1671-1681.

[52] ZHANG K, Gool L V, Timofte R. Deep unfolding network for image super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 3217-3226.

[53] LI M, HSU W, XIE X, et al. SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network[J]. IEEE Transactions on Medical Imaging, 2020, 39(7): 2289-2301.

[54] LIANG J, CAO J, SUN G, et al. Swinir: Image restoration using swin transformer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 1833-1844.

[55] ZHU Y, ZHANG K, LIANG J, et al. Denoising diffusion models for plug-and-play image restoration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 1219-1229.