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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Underwater Image Enhancement with the Low-Rank Nonnegative Matrix Factorization Method
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Underwater Image Enhancement with the Low-Rank Nonnegative Matrix Factorization Method

机译：水下图像增强利用低秩非负矩阵分解方法

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摘要

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.
机译：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。 为了获得培训的地面真理数据也是一个公开问题。 因此，学习过程不可用。 在本文中，我们提出了一种低级非负矩阵分子（LR-NMF）方法，其仅使用劣化的水下图像作为输入以产生更清晰逼真的图像。 根据水下图像形成模型，可以将劣化的水下图像分成三个部分，定向部件，背部和前向散射部件。 后两部分可以被认为是散射。 定向组件被约束为具有低等级。 之后，获得恢复的水下图像。 定量和定性分析说明所提出的方法等同于或更优于最先进的方法。 然而，在没有培训过程的情况下实施很简单。

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