Color correction and restoration based on multi-scale recursive network for underwater optical image |
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| Affiliation: | 1. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China;2. Key Laboratory of Opto-electronics Information Technology, Ministry of Education, Tianjin 300072, China;3. Joint Laboratory for Ocean Observation and Detection, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;4. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;5. School of Computer Science and Technology, Tianjin University, Tianjin 300350, China;6. Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China;1. Department of Computer Science, City University of Hong Kong (CityU), Hong Kong;2. Data61, CSIRO, ACT 2601, Australia;3. Australian National University, Canberra ACT 2600, Australia;4. Research School of Engineering, The Australian National University, Canberra, ACT 0200, Australia |
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| Abstract: | Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images. |
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| Keywords: | Underwater image Style transfer Color correction Image enhancement Convolutional neural network |
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