[1] BERMAN D, LEVY D, AVIDAN S et al. Underwater single image color restoration using haze-lines and a new quantitative dataset[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2822-2837(2021).

[2] ZHANG R, ISOLA P, EFROS A A. Colorful image colorization[C], 649-666(2016).

[3] CHENG Z Z, YANG Q X, SHENG B. Deep colorization[C], 415-423(2015).

[4] CAI B L, XU X M, JIA K et al. DehazeNet: an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 25, 5187-5198(2016).

[5] ZHU J Y, PARK T, ISOLA P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2223-2232(2017).

[6] YI Z L, ZHANG H, TAN P et al. DualGAN: unsupervised dual learning for image-to-image translation[C], 2849-2857(2017).

[7] LI J, SKINNER K, EUSTICE R M et al. WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 3, 387-394(2018).

[8] ISOLA P, ZHU J Y, ZHOU T H et al. Image-to-image translation with conditional adversarial networks[C], 1125-1134(2017).

[9] FABBRI C, ISLAM M, SATTAR J. Enhancing underwater imagery using generative adversarial networks[C], 7159-7165(2018).

[10] MIRZA M, OSINDERO S. Conditional generative adversarial nets[J/OL]. arXiv, 1411-1784(2014).

[11] ISLAM M J, XIA Y Y, SATTAR J. Fast underwater image enhancement for improved visual perception[J]. IEEE Robotics and Automation Letters, 5, 3227-3234(2020).

[12] LI C Y, GUO J C, GUO C L. Emerging from water: underwater image color correction based on weakly supervised color transfer[J]. IEEE Signal Processing Letters, 25, 323-327(2018).

[13] YU X L, QU Y Y, HONG M. Underwater-GAN: underwater image restoration via conditional generative adversarial network[C], 66-75(2019).

[14] LIU P, WANG G Y, QI H et al. Underwater image enhancement with a deep residual framework[J]. IEEE Access, 7, 94614-94629(2019).

[15] YANG H H, HUANG K C, CHEN W T. LAFFNet: a lightweight adaptive feature fusion network for underwater image enhancement[C], 685-692(2021).

[16] FU X Y, CAO X Y. Underwater image enhancement with global-local networks and compressed-histogram equalization[J]. Signal Processing: Image Communication, 86, 115892(2020).

[17] ISLAM M J, ENAN S S, LUO P G et al. Underwater image super-resolution using deep residual multipliers[C], 900-906(2020).

[18] ZHANG W D, DONG L L, ZHANG T et al. Enhancing underwater image via color correction and Bi-interval contrast enhancement[J]. Signal Processing: Image Communication, 90, 116030(2021).

[19] LUO W L, DUAN S Q, ZHENG J W. Underwater image restoration and enhancement based on a fusion algorithm with color balance, contrast optimization, and histogram stretching[J]. IEEE Access, 9, 31792-31804(2021).

[20] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C], 234-241(2015).

[21] WANG J L, FU X S, HANG Z C et al. Multi-type cooperative targets detection using improved YOLOv2 convolutional neural network[J]. Optics and Precision Engineering, 28, 251-260(2020).

[22] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[J/OL]. arXiv, 2004-10934(2020).

[23] JOHNSON J, ALAHI A, FEI-FEI L. Perceptual losses for real-time style transfer and super-resolution[C], 694-711(2016).

[24] IGNATOV A, KOBYSHEV N, TIMOFTE R et al. Dslr-quality photos on mobile devices with deep convolutional networks[C], 3277-3285(2017).