Fig. 1 |. Evaluation of crappifiers with different noise injection on EM data.

a, Different crappifiers applied to high resolution, high SNR images, including “No noise” (no added noise, downsampled pixel size only), “Poisson”, “Gaussian”, and “Additive Gaussian” noise. The real-world acquired low- (LR acquired) and high-resolution (Ground Truth) images are also shown for comparison. Each training set contains 40 image pairs, achieving similar results. b, Visualized restoration performance of PSSR models that were trained on each different crappifier (No noise, Poisson, Gaussian, and Additive Gaussian), as well as a model trained with manually acquired low-resolution versions of the same samples used for the high-resolution semi-synthetic training data (“Real-world Training Data”). Results from a model that using the same crappifier as “Additive Gaussian”, but with ~80x more training data (“Additive Gaussian (~80x)”) are also displayed. LR input and Ground truth of the example testing ROI are also shown. Experiments were repeated with 8–16 images, achieving similar results. PSNR (c), SSIM (d) and resolution as measured by Fourier Ring Correlation analysis (FRC) (e) (PSNR and SSIM, n = 8 independent images; FRC resolution, n = 16 independent images). f, Shown is a table that compares the devoted time, cost and difficulty level between experiments with manually acquired training pairs and experiments using our crappification method. All values are shown as mean ± SEM. ns = not significant. P values are specified in the figure for 0.0001<p<0.05. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, ns = not significant; Two-sided paired t-test.