Well, you can subtract the values of the image with more noise from the image with a bit less noise, thereby predicting the noise \epsilon that is added to, at the end, obtain a noisy image with a Gaussian distribution. In the denoising process, this predicted \epsilon can then be used to distill images from Gaussian distributed noise by subtracting noise. This is how I understand the presentation in original paper (e.g. p. 4, p. 8).