Single image enhancement in sandstorm weather via tensor least square
Guanlei Xu, Xiaotong Wang, Xiaogang Xu
Abstract
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.