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中文摘要：
提出了基于非下采样Shearlet变换和加权非负矩阵分解的红外热波图像融合方法.红外热波序列图像经非下采样Shearlet变换后,采用动态加权非负矩阵分解算法对低频系数进行融合处理.该算法的加权系数依据图像像素突变度动态调整,以突出红外热波图像的缺陷区域;高频系数则采取基于区域改进拉普拉斯能量和的融合策略,以保持缺陷的边缘细节.实验结果表明,本文方法在主观视觉效果及边缘保持度、相关度、运行时间三种客观定量评价指标中,融合性能更优,具有快速、有效等优点,能更完整和清晰地保持红外热波图像的边缘轮廓.该方法可有效地应用于多幅红外热波序列图像的融合中,在红外热波无损检测领域具有较高的实用价值.
英文摘要：
A fusion method of infrared thermal wave images based on nonsubsampled shearlet transform and weighted non-negative matrix factorization was proposed.After infrared thermal wave image sequences are decomposed by nonsubsampled shearlet transform,dynamic weighted non-negative matrix factorization algorithm is adopted for fusion processing of low-frequency coefficients.The weighted coefficients of algorithm are adjusted dynamically according to the mutation degree of image pixel.As a result,the defect areas of infrared thermal image are highlighted.The fusion rule based on area summodified-laplacian is used for high-frequency coefficients to preserve the defect edges and details.Experimental results show that,the method proposed in this paper has superior performance in subjective visual effect and objective quantitative evaluation indices such as edge preserving degree,correlation and running time.The proposed method is fast and effective.It can keep the edges of infrared thermal images more complete and clear.In addition,the proposed method can be adopted for image fusion of multi infrared thermal image sequences effectively.Therefore,the proposed method has high practical value in the thermal wave nondestructive testing.
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