Correlation is very similar to the convolution operation in the sense that it also takes an input image and another kernel and traverses the kernel window through the input by computing a weighted combination of pixel neighborhood values with the kernel values and producing the output image.
The only difference is that, unlike correlation, convolution flips the kernel twice (with regards to the horizontal and vertical axis) before computing the weighted combination.
The next diagram mathematically describes the difference between correlation and convolution on an image:
The SciPy signal module's correlated2d() ...
