Figure 5
From: Multi-resolution convolutional neural networks for inverse problems
Extended Figure: MCNN gives good results in low-frequency domains. The simulated intensities are presented in the first row, and their predictions are presented in the second row (with their MAE shown in the upper right corner). The ground truths are presented in the third row for a visual comparison. The last row shows the Fourier ring correlations between the predictions and the ground truths. In (a), the inputs are defocused images, in which low-frequency details are prevalent. The low-frequency features recover well in this case. In (b), the inputs are intensity gradients, in which high-frequency details dominates. The high-frequency features are largely recovered in this experiment. In (c,d), the inputs are astigmatic images, simulated by rotating a cylinder lens with different angles. Multiple rotations can improve the quality of the output phases, as are shown in the last row of (c,d), but not so much in the high frequencies.
