Optics and Precision Engineering, Volume. 30, Issue 10, 1217(2022)
Single-image translation based on multi-scale dense feature fusion
[1] [1] 1吕晓琪, 吴凉, 谷宇, 等. 基于三维卷积神经网络的低剂量CT肺结节检测[J]. 光学 精密工程, 2018, 26(5): 1211-1218. doi: 10.3788/OPE.20182605.1211LVX Q, WUL, GUY, et al. Detection of low dose CT pulmonary nodules based on 3D convolution neural network[J]. Opt. Precision Eng., 2018, 26(5): 1211-1218.(in Chinese). doi: 10.3788/OPE.20182605.1211
[2] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).
[3] ZHANG R, ISOLA P, EFROS A A. Colorful image colorization[C], 649-666(2016).
[4] [4] 4杜振龙, 沈海洋, 宋国美, 等. 基于改进CycleGAN的图像风格迁移[J]. 光学 精密工程, 2019, 27(8): 1836-1844. doi: 10.3788/ope.20192708.1836DUZ L, SHENH Y, SONGG M, et al. Image style transfer based on improved CycleGAN[J]. Opt. Precision Eng., 2019, 27(8): 1836-1844.(in Chinese). doi: 10.3788/ope.20192708.1836
[5] PARK T, EFROS A A, ZHANG R et al. Contrastive learning for unpaired image-to-image translation[C], 319-345(2020).
[6] [6] 6李宇, 刘雪莹, 张洪群, 等. 基于卷积神经网络的光学遥感图像检索[J]. 光学 精密工程, 2018, 26(1): 200-207. doi: 10.3788/OPE.20182601.0200LIY, LIUX Y, ZHANGH Q, et al. Optical remote sensing image retrieval based on convolutional neural networks[J]. Opt. Precision Eng., 2018, 26(1): 200-207.(in Chinese). doi: 10.3788/OPE.20182601.0200
[7] GOODFELLOW I, POUGET A J, MIRZA M et al. Generative adversarial nets[J]. Advances in neural information processing systems, 27(2014).
[8] ZHU J Y, PARK T, ISOLA P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2242-2251(2017).
[9] KIM T, KIM H et al. Learning to discover cross-domain relations with generative adversarial networks[C], 1865(2017).
[10] YI Z L, ZHANG H, TAN P et al. DualGAN: unsupervised dual learning for image-to-image translation[C], 2868-2876(2017).
[11] SHAHAM T R, DEKEL T, MICHAELI T. SinGAN: learning a generative model from a single natural image[C], 4569-4579(2019).
[12] LIN J X, PANG Y X, XIA Y C et al. TuiGAN: learning versatile image-to-image translation with two unpaired images[C], 18-35(2020).
[13] HUANG G, LIU Z, MAATEN LVAN DER et al. Densely connected convolutional networks[C], 2261-2269(2017).
[15] HINZ T, FISHER M, WANG O et al. Improved techniques for training single-image GANs[C], 1299-1308(2021).
[16] ZHENG C X, CHAM T J, CAI J F. The spatially-correlative loss for various image translation tasks[C], 16402-16412(2021).
[17] LEE H Y, TSENG H Y, HUANG J B et al. Diverse image-to-image translation via disentangled representations[C], 35-51(2018).
[18] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[19] GULRAJANI I, AHMED F, ARJOVSKY M et al. Improved training of wasserstein gans[J]. arXiv preprint arXiv:, 2017.
[20] PUMAROLA A, AGUDO A, MARTINEZ A M et al. GANimation: anatomically-aware facial animation from a single image[J]. Computer Vision-ECCV:European Conference on Computer Vision: Proceedings European Conference on Computer Vision, 11214, 835-851(2018).
[21] HEUSEL M, RAMSAUER H, UNTERTHINER T et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium[J]. Advances in neural information processing systems(2017).
[22] NEWEY W K. Adaptive estimation of regression models via moment restrictions[J]. Journal of Econometrics, 38, 301-339(1988).
Get Citation
Copy Citation Text
Qihang LI, Long FENG, Qing YANG, Yu WANG, Guohua GENG. Single-image translation based on multi-scale dense feature fusion[J]. Optics and Precision Engineering, 2022, 30(10): 1217
Category: Information Sciences
Received: Dec. 22, 2021
Accepted: --
Published Online: Jun. 1, 2022
The Author Email: Guohua GENG (1925995331@qq.com)