Enhanced Confocal Microscopy with Physics-Informed Deep Autoencoders

We present a physics-informed deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), including diffraction-limited resolution, noise, and under sampling due to low laser power conditions. The optical system’s point spread function and primary CLSM image degradation mechanisms, namely photon shot noise, dark current noise, motion blur, speckle noise, and under sampling are explicitly incorporated into the model as physics-based constraints. A convolutional autoencoder is trained with a custom loss function that integrates these optical degradation processes, ensuring that the reconstructed images adhere to physical image formation principles. The model is evaluated on simulated CLSM datasets generated based on experimentally observed CLSM noise characteristics. Statistical comparisons, including intensity histograms, spatial frequency distributions, and structural similarity metrics, confirm that the synthetic dataset closely matches accurate CLSM data. The proposed approach is compared with traditional image reconstruction methods, including Richardson-Lucy deconvolution, non-negative least squares, and total variation regularization. Results indicate that the physics-constrained autoencoder improves structural detail recovery while maintaining consistency with known CLSM imaging physics. This study demonstrates that physics-informed deep learning can provide an alternative computational approach to CLSM enhancement, complementing existing optical correction methods. Future work will focus on further validation using experimental CLSM acquisitions.