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王柯俨, 黄诗芮, 李云松(西安电子科技大学综合业务网理论及关键技术国家重点实验室, 西安 710071)

摘 要
An optical reconstruction based underwater image analysis

Wang Keyan, Huang Shirui, Li Yunsong(State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China)

Underwater optical images have played a key role in related to marine resource development, marine environment protection and marine engineering. Due to the harsh and complex underwater environment, the raw underwater optical images are challenged to the quality degradation, which are mainly introduced via an underwater scenario of light selective absorption and scattering. The degraded underwater images have low contrast, blurred details and color distortion, which severely restrict its performance and applications of underwater intelligent processing systems. In particular, the current deep learning based underwater image recovering has been facilitated currently. Our review first analyzes the mechanism of underwater image degradation, as well as describes the existing underwater imaging models and summarizes the challenges of underwater image reconstruction. Then, we trace the evolving of underwater optical image reconstruction methods. In accordance with the deep learning technology and the physical imaging models contextual, the existing algorithms are segmented into four categories for underwater images recovering methods, which are traditional image enhancement, traditional image restoration, deep-learning-based image enhancement and deep-learning-based image restoration. These four types of existing timescale methods are then discussed and analyzed, including their theories, features, pros and cons, respectively. Specifically, the traditional image enhancement methods tend to deliver unnatural results with color deviations and under-enhancement or over-enhancement of local area to improve the visibility of the underwater images effectively. Conversely, the traditional image restoration methods are based on the mechanism of underwater image degradation and a challenging issue is constrained of the limitations of imaging models and priori knowledge in diverse underwater scenarios. The deep learning based restoration methods is facilitated to nonlinear fitting capabilities of neural networks with an error stacking barrier and insufficient restoration results. The deep-learning-based enhancement methods are comparatively robust and suitable to recover diverse underwater images with flexible neural networks. However, they are relatively difficult to converge, and their generalization capability is insufficient. We introduce the current public underwater image datasets, which can be divided into two categories, and summarize their features and applications, respectively. Moreover, we also demonstrate more evaluation metrics for the quality of underwater images. To evaluate their performance quantitatively and qualitatively, we conduct experiments on eight typical underwater image reconstruction methods further. Three benchmarks are optioned for color cast removal, contrast improvement and comprehensive test. Our demonstrated results indicate that none of these methods are robust enough to recover diverse types of underwater images. The reconstruction of better underwater optical images is a big challenge issue of improving the robustness and generalization of reconstruction, developing more lightweight networks or processing algorithms of real-time applications, utilizing underwater image reconstruction to harness vision tasks ability, as well as establishing an underwater images quality assessment system.