[7] [7] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[EB/OL]. 2017: https://arXiv.orglabs/1701.07875.

[8] [8] Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GANs[EB/OL]. 2017: https://arxiv.org/abs/1704.00028.

[14] [14] Yan R, He Q, Liu Y, et al. Unpaired virtual histological staining using prior-guided generative adversarial networks[J]. Comput. Med. Imaging Graphics, 2023, 105: 102185.

[15] [15] Tam T N, Trung T H, Tam M P, et al. Validating functional redundancy with mixed generative adversarial networks[J]. Knowledge-Based Syst., 2023, 264: 110342.

[16] [16] Chen Y, Wang Z, Bai X. Fussy sparse subspace clustering for infrared image segmentation[J]. IEEE Trans. Image Process., 2023, 32: 2132-2023.

[17] [17] Dabov K, Foi A, Katkovnik V, et al. Image denoising by spare 3-D transform-domain collaborative filtering[J]. IEEE Trans. Image Process., 2017, 16(8): 2080-2095.

[18] [18] Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Trans. Image Process., 2017, 26(7): 3142-3155.

[19] [19] Cheng S, Wang Y, Huang H, et al. Nbnet: Noise basis learning for image denoising with subspace projection[C]// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2021: 4896-4906.