Statistical comparison of image fusion algorithms: Recommendations
Statistical comparison of image fusion algorithms: Recommendations
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DOI：
10.1016/j.inffus.2016.12.007
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发表时间：
2017-07-01
影响因子：
18.6
通讯作者：
John, V.
中科院分区：
文献类型：
--
作者：
Liu, Z.;Blasch, E.;John, V.
像素级图像融合已应用于多种应用，包括多模态医学成像、遥感、工业检测、视频监控和夜视等。针对众多应用提出了各种算法，这些应用需要全面的评估方法来辨别哪些方法提供决策支持。目前，新提出的算法的验证或评估是主观或客观进行的。主观评估成本高昂，且受多种难以控制的因素影响。另一方面，使用融合性能度量来进行客观评估，该度量被定义为评估融合操作的有效性和/或效率。从不同的角度，提出了许多针对融合过程的融合度量。大多数图像融合研究都对所提出的和现有的融合算法与多个图像数据集上选定的融合度量进行了比较。所提出的算法的优点是通过与最佳或更好的度量值的相对差异来证明的。然而，这种差异的统计显着性未知，导致对方法之间的定量差异的误解。本文提出使用非参数统计分析来比较融合算法以及采用显着性测试的图像融合工具箱（ImTEST）。提出并推荐了在不同场景中使用不同测试的策略。最近发布的算法的实验证明了采用统计比较来建立图像融合研究基线的必要性。 (C) 2016 Elsevier B.V. 保留所有权利。
Pixel-level image fusion has been applied in a variety of applications, including multi-modal medical imaging, remote sensing, industrial inspection, video surveillance, and night vision etc. Various algorithms are being proposed for numerous applications which requires a comprehensive method of assessment to discern which methods provide decision support. Currently, the validation or assessment of newly proposed algorithms is done either subjectively or objectively. A subjective assessment is costly and affected by a number of factors that are difficult to control. On the other hand, an objective assessment is carried out with a fusion performance metric which is defined to evaluate the effectiveness and/or efficiency of the fusion operation. There are a number of fusion metrics proposed for fusion processes taking different perspectives. Most image fusion research presents a comparison of the proposed and existing fusion algorithms with selected fusion metric(s) over multiple image data sets. The proposed algorithm advantage is justified by the relative difference with the best or better metric values. However, the statistical significance of such difference is unknown leading to a misperception of the quantitative differences between methods. This paper proposes the use of non-parametric statistical analysis for comparisons of fusion algorithms along with the Image fusion Toolbox Employing Significance Testing (ImTEST). Strategies to use different tests in varied scenarios are presented and recommended. ExperimentS with recently published algorithms demonstrate the necessity to adopt the statistical comparison to establish a baseline for image fusion research. (C) 2016 Elsevier B.V. All rights reserved.