消斑结构损失(DSL):一种衡量消斑算法结构保持能力的新指标

Xuezhi Yang, Li Jia, Yujie Wang, Yiming Tang
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摘要

本文提出了一种新的去斑结构损失(DSL)指标,用于去斑算法的性能评估,重点关注结构信息的保存。DSL度量考虑了去斑算法中最佳和最差结构保存的特征,通过在边缘点处使用比率图像与无噪声参考图像之间的局部相关性来检测比率图像中图像结构的存在,从而对去斑算法的结构保存能力进行客观和定量的衡量。采用三种算法对模拟SAR图像的去斑结果进行了DSL度量测试,验证了DSL度量的有效性。相比之下,其他五种常用的去噪指标与去噪结果和比率图像中显示的结构损失不一致。
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Despeckling structural loss(DSL): A new metric for measuring structure-preserving capability of despeckling algorithms
In this paper, a new metric called despeckling structural loss(DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural information. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of image structures in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. The DSL metric has been tested on despeckling results of a simulated SAR image using three types of algorithms and efficiency of the DSL has been demonstrated. In comparison, the other five commonly used despeckling metrics fail to keep a consistency with the structural loss shown in despeckling results as well as ratio images.
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