Noise was added to the input in a few different ways (presented in Fig. 3B). The noise of each panel was generated as follows:

A. No noise.

B. “White” noise: Each pixel was randomly toggled with 5% probability.

C. Random blur: Input was convolved with a Gaussian with a width drawn uniformly between 0 and 3. The array was then thresholded at 0.1. Here and below, “thresholded at z” means a pointwise threshold was imposed on the array, such that values smaller than z were set to 0 and otherwise set to 1.

D. Each pixel was randomly toggled with 1% probability and then passed through random blur (C).

E. Input was random blurred [as in (C)] but thresholded at 0.55. We denote the blurred-and-thresholded input as X. Then, X was noised using both additive and multiplicative noise, as follows: Y and Z are two random fields drawn from a pointwise uniform distribution between 0 and 1 and convolved with a Gaussian of width seven (pixels) and thresholded at 0.55. Last, the “noised” input ismin(X+(1Y), 1)(1Z)

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