The ability to non-destructively visualize transient phenomenon via spatial and temporal statistics of fluctuating speckle patterns is determined primarily by the contrast between regions of differing activity levels. We demonstrate a singular value decomposition-based method for extracting spatio-temporal correlation parameters from temporal sequences of dynamic speckle patterns. Each pattern in the temporal sequence is reduced to a representation using column vectors which are subsequently grouped to form a matrix encoded with localized spatio-temporal speckle intensity data. A correlation metric is defined using the singular values of this matrix and is subsequently utilized for generating a correlation map by interpolating the correlation computed at each individual patch. Via a comparison with synthetic data that we generate using known decorrelation parameters, we are able to show that our algorithm produces activity maps with enhanced contrast that are closest to the ground truth.

通过波动斑点模式的时空统计非破坏性地观察瞬态现象的能力主要取决于不同活动水平区域之间的对比度。我们演示了一种基于奇异值分解的方法，用于从动态散斑图案的时间序列中提取时空相关参数。时间序列中的每个模式都使用列向量简化为表示形式，随后将这些列向量分组以形成使用局部时空散斑强度数据编码的矩阵。使用该矩阵的奇异值定义相关度量，然后将其用于通过内插在每个单独的块处计算的相关来生成相关图。