Due to the scattering and absorption effects in the undersea environment, underwater image enhancement is a challenging problem. To obtain the ground-truth data for training is also an open problem. So, the learning process is unavailable. In this paper, we propose a Low-Rank Nonnegative Matrix Factorization (LR-NMF) method, which only uses the degraded underwater image as input to generate the more clear and realistic image. According to the underwater image formation model, the degraded underwater image could be separated into three parts, the directed component, the back and forward scattering components. The latter two parts can be considered as scattering. The directed component is constrained to have a low rank. After that, the restored underwater image is obtained. The quantitative and qualitative analyses illustrate that the proposed method performed equivalent or better than the state-of-the-art methods. Yet, it’s simple to implement without the training process.

中文翻译：

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基于低秩非负矩阵分解法的水下图像增强

由于海底环境中的散射和吸收效应，水下图像增强是一个具有挑战性的问题。获得用于训练的真实数据也是一个悬而未决的问题。因此，学习过程不可用。在本文中，我们提出了一种低秩非负矩阵分解（LR-NMF）方法，该方法仅使用降级的水下图像作为输入来生成更清晰逼真的图像。根据水下成像模型，退化的水下图像可以分为三部分，定向分量、后向散射分量和前向散射分量。后两部分可以认为是散射。有向分量被约束为具有低秩。之后，得到恢复的水下图像。定量和定性分析表明，所提出的方法与最先进的方法相当或更好。然而，无需培训过程即可轻松实施。