Research on Underwater Image Enhancement Technology Based on Generative Adversative Net work s

Zuo, Geng-ren; Yin, Bo; Wang, Xiao; Lan, Zehua

doi:10.12783/dtcse/cnai2018/24184

Cited by 1 publication

(1 citation statement)

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“…Deep learning methods. With the proposal of Generative Adversarial Networks [9], researchers have focused on transforming overwater and underwater images. Zhao et al used a jointly trained generative adversarial network for underwater image synthesis and depth map estimation to synthesize realistic underwater images by transferring RGB-D images to multi-style underwater images while preserving object and structural information from aerial images [3].…”

Section: Underwater Image Synthesis Modelmentioning

confidence: 99%

Underwater Image Enhancement Based on Hybrid Enhanced Generative Adversarial Network

Xu,

Zhou,

Liu

et al. 2023

JMSE

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In recent years, underwater image processing has played an essential role in ocean exploration. The complexity of seawater leads to the phenomena of light absorption and scattering, which in turn cause serious image degradation problems, making it difficult to capture high-quality underwater images. A novel underwater image enhancement model based on Hybrid Enhanced Generative Adversarial Network (HEGAN) is proposed in this paper. By designing a Hybrid Underwater Image Synthesis Model (HUISM) based on a physical model and a deep learning method, many richly varied paired underwater images are acquired to compensate for the missing problem of underwater image enhancement dataset training. Meanwhile, the Detection Perception Enhancement Model (DPEM) with Perceptual Loss is designed to transfer the coding knowledge in the form of the gradient to the enhancement model through the perceptual loss, which leads to the generation of visually better and detection-friendly underwater images. Then, the synthesized and enhanced models are integrated into an adversarial network to generate high-quality underwater clear images through game learning. Experiments show that the proposed method significantly outperforms several state-of-the-art methods both qualitatively and quantitatively. Furthermore, it is also demonstrated that the method can improve target detection performance in underwater environments, which has specific application value for subsequent image processing.

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Section: Underwater Image Synthesis Modelmentioning

confidence: 99%