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Table 3

Overview of the mean results of the three evaluation methods with clean, noisy and noisy plus additional white noise input maps and for input maps with different mask filling.

Jet offset [°] Intensity deviation [%] Source area ratio
Noiseless input Vnoiseless −0.02 ± 1.43 −4.35 ± 6.66 0.96 ± 0.12
Noisy input Vnoisy −0.01 ± 1.33 0.61 ± 12.40 0.99 ± 0.13
Noise & white noise Vnoisy&white noise 0.05 ± 1.96 3.49 ± 12.26 1.00 ± 0.13

Sampling Density

20% −0.07 ± 3.14 −8.99 ± 16.26 0.85 ± 0.19
50% 0.02 ± 1.96 6.69 ± 14.45 0.92 ± 0.17
70% 0.01 ± 3.12 11.94 ± 63.60 0.91 ± 0.17

Notes. A new training set is used for different noise models. The data sets consist of 50 000 training images, 10 000 validation images, and 10 000 test images. For different sampling densities, we do not train a new model but create dedicated test data sets consisting of 10 000 test amplitude and phase maps. The evaluation is done using the deep learning model trained on noisy input data.

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