So I am using the Source Extractor to find point sources in my images and it offers the possibility to use matched filtering to enhance the detection results. In the documentation they also claim that the matched filter defaults to a convolution of the data, should the noise be equal across the field (please correct me if I got this wrong). While there is a nice and easy to understand visual reprentation for the convolution, where the convoluted pixel value is simply the sum of the pixel values "below" the kernel weighted by the factors in the kernel "above", I have yet to find a similar explanation for what is going on in matched filtering.
I also like how the matched filter is derived in terms of matrices, since I feel like this makes the most sense with image processing (unlike the derivation using itegrals I found in many places), but I haven't found a book yet where this is well explained. Can someone point me to a source where matched filtering is explained regarding image processing?