In this paper, we present a tensor least square based model for sand / sandstorm removal in images. The main contributions of this paper are as follows. First, an important intrinsic natural feature of outdoor scenes free of sand / sandstorm is found that the outlines in RGB channels are somewise similar, which discloses the physical validation using the tensor instead of the matrix. Second, a tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details. This model not only decomposes the color image ( taken as an inseparable indivisibility ) in X, Y directions, but also in Z direction, which meets the statistical feature of natural scenes and can physically disclose the intrinsic color information. The modelʼ s advantages are twofold: one is the decomposition of edge-preserving base layers and details that can be employed for contrast enhancement without artificial halos, and the other one is the color driving ability that makes the enhanced images as close to natural images as possible via the inherent color structure. Thirdly, the tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images. Finally, the experiments and comparisons with the state-of-the-art methods on real degraded images under sandstorm weather are shown to verify our methodʼ s efficiency.

中文翻译：

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通过张量最小二乘增强沙尘暴天气中的单幅图像

在本文中，我们提出了基于张量最小二乘的模型，用于去除图像中的沙尘暴。本文的主要贡献如下。首先，发现无沙/沙尘暴的室外场景的一个重要的内在自然特征是，RGB通道中的轮廓在某种程度上是相似的，这揭示了使用张量而不是矩阵进行物理验证。其次，提出了一个张量最小二乘优化模型，用于分解保留边缘的基础层和细节。该模型不仅可以在X，Y方向上分解彩色图像（作为不可分割的不可分割性），而且可以在Z方向上分解，这符合自然场景的统计特征，并且可以物理地公开固有的颜色信息。该模型的优点是双重的：一种是保留边缘的基础层和细节的分解，可在没有人造光晕的情况下用于对比度增强，另一种是颜色驱动能力，通过固有的颜色结构使增强的图像尽可能接近自然图像。第三，讨论了基于张量最小二乘优化模型的沙尘暴天气图像增强方案。最后，显示了在沙尘暴天气下对真实退化图像进行的最新技术实验和比较，证明了我们方法的有效性。讨论了基于张量最小二乘优化模型的沙尘暴天气图像增强方案。最后，显示了在沙尘暴天气下对真实退化图像进行的最新技术实验和比较，证明了我们方法的有效性。讨论了基于张量最小二乘优化模型的沙尘暴天气图像增强方案。最后，显示了在沙尘暴天气下对真实退化图像进行的最新技术实验和比较，证明了我们方法的有效性。