The sparse representation model is widely used in the field of computer vision and pattern recognition. The l-1 norm of sparse coefficients in this model is successfully applied in the field of sparse representation-based image fusion as an activity level measurement (ALM) feature of image patches, but the physical meaning of this parameter is not clearly explained so far. Starting from this issue, firstly, from a generalized viewpoint, the l-1 norm of the sparse coefficient vector of an image patch is defined as the singularity; secondly, the rationality of the definition is qualitatively explained in theory; thirdly, the physical meaning of the parameter is shown in a graphical way through experiments. Theoretical and experimental analysis results show that the sparse coefficient of the signal vector can be regarded as the projection coordinate of the signal under the dictionary basis function, and the l-1 norm of the sparse coefficient can be used to describe the singularity characteristics of a signal.