Noise Rejection Through An Improved Quantum Illumination Protocol Data
All frames saved in the archive are 128x128 pixels.

Bird in a Cage analysis used in the generation of Figure 2: Data in folder BirdCageDataBinarised

1. For each frame take the two beams as regions of interest (ROIs) such that the correlation peak is centered on the central pixel
2. Rotate one by an angle of pi (or equivalently invert in x and y)
3. Perform an AND operation and use result to build the AND-images I_AND. I_AND = C_Q + C_U
4. Perform a bitwise multiplication of the reference beam and the probe beam / total number of frames to get the accidental correlations C_U
5. Subtract the accidental correaltions from the AND-image to obtain the image of the quantum correlations C_Q.

6. Perform steps 1-5 for the reference beam offset by a total of nine steps so as to collect correlations that arrive in the 3x3 grid about the central correlation point. 
steps = [0, 0; 0 1; 0, -1; 1, 0; -1, 0; 1, 1; 1, -1; -1, -1; -1, 1];
7.Sum the resulting 9 images of AND-image with accidentals subtracted

8. Mask the image using Masks\BackgroundMask.txt and calculate the mean value of the regions of the image that do not comprise either the bird or the cage. This value corresponds to the floor and the other values below are relative to this so as to assess the relative brightnesses of the bird and the cage to calculate the noise rejection ratio and the distinguishability metric.
9. Using Masks\ClassicalCageMask.txt Masks\QuantumBirdMask.txt, calculate the mean value of the classical cage <S>, and the mean value and standard deviation of the quantum bird regions <O> and sigma_O. 
10. Assess distinguishability ratio (D) by taking the ratio of regions containing only the quantum illuminated object <O> and regions containing only the classically illuminated cage <S> plus the standard deviation on the regions of the object sigma_O. Compare the ratio for the classical and quantum illumination AND-image according to the text and equation 4 in the manuscript.