  Using the example of configurations generated with the worm algorithm for the
two-dimensional Ising model, we propose renormalization group (RG)
transformations, inspired by the tensor RG, that can be applied to sets of
images. We relate criticality to the logarithmic divergence of the largest
principal component. We discuss the changes in link occupation under the RG
transformation, suggest ways to obtain data collapse, and compare with the two
state tensor RG approximation near the fixed point.
