Underwater image enhancement using improved generative adversarial network. (29th June 2020)

• Record Type: Journal Article
• Authors: Zhang, Tingting; Li, Yujie; Takahashi, Shinya
• Other Names: Jeon Gwanggil guestEditor.; Chehri Abdellah guestEditor.; Lu Huimin guestEditor.; Guna Jože guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 22(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-29
• Subjects: color restoration -- GAN -- image enhancement -- loss function; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5841
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The generative adversarial network is widely used in image generation, and the generation of images with different styles is applied to underwater image enhancement. The existing underwater image generative adversarial network does not realize color correction when processing underwater images Therefore, we propose an improved generative adversarial network for image color restoration. Firstly, the loss function in the network is improved to train the dataset. Then the improved network is used to detect the underwater image. After network testing, the underwater image is more satisfactory than the traditional image. Numerical results show that this method has a good color restoration and sharpening effects.