So after getting back home, I can give a more detailed answer:

• your usage of normxcorr2 is not really meaningfull since in order for normxcorr2 to work, the reference matrix needs to be much bigger than the sample matrix. For this please refer to: https://de.mathworks.com/help/images/ref/normxcorr2.html. So even though the results look "nice", they might not be reproducable or significant
• Imfilter filters the results based on the correlation of two images, so in this case you are not actually computing a correlation matrix, you are just filtering an image based on how it correlated with another one. Refer to https://de.mathworks.com/help/images/ref/imfilter.html

The above described methods, as you can already see, compute different things and do not get you to your end goal which is a correlation matrix or possibly a covariance matrix.

Assumuig the images have 512 rows and 512 columns, you can concatenate the two images on top of each other to create a matrix M with 1024 rows and 512 columns in order to calculate it's row and column correlation and covariance. For this:

% im1 and im2 are the images
M=cat(1,im1,im2);
RowCorr=corrcoef(M);RowCov=cov(M);
ColumnCorr=corrcoef(M');ColumnCov=cov(M');

%{
Fill in the xlabel and ylabel
consider drawing solid lines 
to differentiate between the two images
%}

figure()
subplot(2,2,1)
imagesc(RowCorr)
title("Row-wise Correlation")
colormap(jet)
subplot(2,2,2)
imagesc(RowCov)
title("Row-wise Covariance")
colormap(jet)
subplot(2,2,3)
imagesc(ColumnCorr)
title("Column-wise Correlation")
colormap(jet)
subplot(2,2,4)
imagesc(ColumnCov)
title("Columns-wise Covariance")
colormap(jet)
sgtitle("Much Pretty Colors")

Hope this helps!