how to understand large kernel convolution decomposition？

[Rainbowman0]

Thank you so much for this enlightening and inspiring work.I don't quite understand why LKC(i.e. Large Kernel Convolution) = DW-Conv + DW-D-Conv + 1x1Conv.

My current understanding can refer to the following table:

So according to my understanding, what you want to express in Fig.2 is: DW-Conv, DW-D-Conv and 1x1Conv each have a part of LKC properties, and have lower computational complexity.That is to say, the plus sign and equal sign in Fig.2 are for the addition of Properties.

Is my understanding correct? If it is not correct, please give a more intuitive understanding. Thank you very much!