Y. Guo, H. Li and P. Zhuang, "Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network," in IEEE Journal of Oceanic Engineering, vol. 45, no. 3, pp. 862-870, July 2020, doi: 10.1109/JOE.2019.2911447.
We propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile, nonsaturating GAN loss function combining L 1 loss and gradient loss is presented to focus on image features of ground truth.
Besides, we test our method on the large-scale real-world underwater image enhancement benchmark dataset (UIEBD), Underwater Image Quality Set (UIQS), and Underwater Color Cast Set (UCCS). Please enjoy the 890 enhanced images (~103MB) on UIEBD and the 7560 enhanced images (~154MB) on UIQS and UCCS.
The entire enhanced results by the proposed method. Google Drive Link
Li C, Guo C, Ren W, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2019, 29: 4376-4389.
Liu R, Fan X, Zhu M, et al. Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020.
Guo Y, Li H, Zhuang P. Underwater image enhancement using a multiscale dense generative adversarial network[J]. IEEE Journal of Oceanic Engineering, 2019.