[1] Cai C D, Huo G Y, Zhou Y et al. Underwater image restoration method based on scene depth estimation and white balance[J]. Laser & Optoelectronics Progress, 56, 031008(2019).
[2] Guo Y C, Li H Y, Zhuang P X et al. Underwater image enhancement using a multiscale dense generative adversarial network[J]. IEEE Journal of Oceanic Engineering, 45, 862-870(2020).
[3] Liu P, Wang G Y, Qi H et al. Underwater image enhancement with a deep residual framework[J]. IEEE Access, 7, 94614-94629(2019).
[4] Fabbri C, Islam M J, Sattar J et al. Enhancing underwater imagery using generative adversarial networks[C]. //2018 IEEE International Conference on Robotics and Automation (ICRA), May 21-25, 2018, Brisbane, QLD, Australia., 7159-7165(2018).
[5] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2242-2251(2017).
[6] Jin W P, Guo J C, Qi Q et al. Underwater image enhancement based on conditional generative adversarial network[J]. Laser & Optoelectronics Progress, 57, 141002(2020).
[7] Li C Y, Guo C L, Ren W Q et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 29, 4376-4389(2020).
[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).
[9] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).
[10] Huang G, Liu Z, Laurens V D M. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).
[11] Lin S, Liu S B, Tang Y D et al. Multi-input fusion adversarial network for underwater image enhancement[J]. Infrared and Laser Engineering, 49, 217-225(2020).
[12] Yang M, Sowmya A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 24, 6062-6071(2015).
[13] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision -ECCV 2016. Lecture notes in computer science, 9906, 694-711(2016).
[14] Wang S H, Phillips P, Sui Y X et al. Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling[J]. Journal of Medical Systems, 42, 1-11(2018).
[15] Bianco G, Muzzupappa M, Bruno F et al. A new color correction method for underwater imaging. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W5, 25-32(2015).
[16] Drews P L J, Nascimento E R, Botelho S S C et al. Underwater depth estimation and image restoration based on single images[J]. IEEE Computer Graphics and Applications, 36, 24-35(2016).
[17] Pan P W, Yuan F, Cheng E et al. Underwater image de-scattering and enhancing using dehazenet and hwd[J]. Journal of Marine Science and Technology, 26, 531-540(2018).
[18] Islam M J, Xia Y Y, Sattar J et al. Fast underwater image enhancement for improved visual perception[J]. IEEE Robotics and Automation Letters, 5, 3227-3234(2020).
[19] Panetta K, Gao C, Agaian S et al. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 41, 541-551(2016).
[20] Dai C G, Lin M X, Wang Z et al. Color compensation based on bright channel and fusion for underwater image enhancement[J]. Acta Optica Sinica, 38, 1110003(2018).