Underwater Image Enhancement Based on Pyramid Attention Mechanism and Generative Adversarial Network

Yue Wang; Dexing Wang; Hongchun Yuan; Ruoyou Wu; Peng Gong

doi:10.3788/LOP202158.1610006

[1] Shkurti F, Xu A Q, Meghjani M et al. Multi-domain monitoring of marine environments using a heterogeneous robot team[C]. //2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 7-12, 2012, Vilamoura-Algarve, Portugal., 1747-1753(2012).

[2] Hou G J. Research on underwater image enhancement and object recognition algorithms[D], 19-21(2015).

[3] Jin W P, Guo J C, Qi Q. Underwater image enhancement based on conditional generative adversarial network[J]. Laser & Optoelectronics Progress, 57, 141002(2020).

[4] Güraksin G E, Köse U, Deperlıoğlu Ö. Underwater image enhancement based on contrast adjustment via differential evolution algorithm[C]. //2016 International Symposium on INnovations in Intelligent Systems and Applications (INISTA), August 2-5, 2016, Sinaia, Romania., 1-5(2016).

[5] Iqbal K, Odetayo M, James A et al. Enhancing thelow quality images using Unsupervised Colour Correction Method[C]. //2010 IEEE International Conference on Systems, Man and Cybernetics, October 10-13, 2010, Istanbul, Turkey., 1703-1709(2010).

[6] 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).

[7] Zou P Y, Zhang W D, Shi J Y et al. Underwater image enhancement algorithm based on fusion of high and low frequency components[J]. Laser & Optoelectronics Progress, 57, 161010(2020).

[8] Drews P, Jr, do Nascimento E, Moraes F et al. Transmission estimation in underwater single images[C]. //2013 IEEE International Conference on Computer Vision Workshops, December 2-8, 2013, Sydney, NSW, Australia., 825-830(2013).

[9] Chiang J Y, Chen Y C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE Transactions on Image Processing, 21, 1756-1769(2012).

[10] Yang H Y, Chen P Y, Huang C C et al. Low complexity underwater image enhancement based on dark channel prior[C]. //2011 Second International Conference on Innovations in Bio-inspired Computing and Applications, December 16-18, 2011, Shenzhen, China., 17-20(2011).

[11] 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).

[12] Li C Y, Anwar S, Porikli F. Underwater scene prior inspired deep underwater image and video enhancement[J]. Pattern Recognition, 98, 107038(2020).

[13] 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).

[14] Fabbri C, Islam M J, Sattar J. 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).

[15] Yu X L, Qu Y Y, Hong M. Underwater-GAN:underwater image restoration via conditional generative adversarial network[M]. //Zhang Z X, Suter D, Tian Y L, et al. Pattern recognition and information forensics. Lecture notes in computer science, 11188, 66-75(2019).

[16] Liu P, Wang G Y, Qi H et al. Underwater image enhancement with a deep residual framework[J]. IEEE Access, 7, 94614-94629(2019).

[17] 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).

[18] Li H C, Xiong P F, An J et al. Pyramid attention network for semantic segmentation[EB/OL]. (2018-11-25) [2020-10-10]. https://arxiv.org/abs/1805.10180v1

[19] Ronneberger O, Fischer P, Brox T. U-net:convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[20] Li C, Wand M. Precomputed real-time texture synthesis with Markovian generative adversarial networks[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9907, 702-716(2016).

[21] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5967-5976(2017).

[22] 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).

[23] Ignatov A, Kobyshev N, Timofte R et al. DSLR-quality photos on mobile devices with deep convolutional networks[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 3297-3305(2017).

[24] Chen Y S, Wang Y C, Kao M H et al. Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 6306-6314(2018).

[25] Horé A, Ziou D. Image quality metrics: PSNR vs. SSIM[C]. //2010 20th International Conference on Pattern Recognition, August 23-26, 2010, Istanbul, Turkey, 2366-2369(2010).

[26] Panetta K, Gao C, Agaian S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 41, 541-551(2016).

[27] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 20, 209-212(2013).

[28] Liu L X, Hua Y, Zhao Q J et al. Blind image quality assessment by relative gradient statistics and adaboosting neural network[J]. Signal Processing: Image Communication, 40, 1-15(2016).