Fig. 4.

Performance evaluation. (a) Performance comparison between Deep Neural Network (DNN), and traditional autofocus measures across 420 test cases. While only 2 defocused images are provided to the DNN, the traditional autofocus methods receive as an input 13 images. In both cases, the spacing between two consecutive images is 6 µm. On a single image patch with a size of ∼83 × 83 µm² the DNN outperforms traditional autofocus measures, while on larger image patches (250 × 250 µm²) the DNN and DCTS achieve comparable results. Please note that for the larger image patch the DNN performs its calculation on nine (83 × 83 µm²) patches, and results with certainty (cert) above 0.35 are averaged to achieve the final prediction. (b) Representative examples of defocus level prediction $(\mathrm{\Delta }{z}_{predict})$ by the DNN on the test dataset (single patch). Each box shows an individual and independent image patch, and the color of the border indicates the $\mathrm{\Delta }{z}_{predict}$ value. If the certainty of an image patch is lower than 0.35, the colored border is deleted, and this patch is discarded.