According to some properties, of SVD, each singular value (SV) specifies the luminance of the SVD image layer, whereas the respective pair of singular vectors specifies image geometry.

• 图像奇异值分解（SVD）有以下性质：分解后图像矩阵的奇异值集中反映了图像的“亮度”（能量）特性，而对应的奇异矩阵只反映了图像的“几何”特性。