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Neural networks usually need a huge amount of training data, so the possibilities of transfer learning for a reduction of the dataset size were investigated. In addition, we also present first studies the interpretability of the trained network to find ideal image acquisition techniques and optimize the training.
Axial scanning and spherical aberration correction in confocal microscopy employing an adaptive lens
In this contribution we propose a compact low cost lensless digital holographic microscope capable of performing measurements on reflective microstructures. The novelty of the system consists on a direct use of a laser diode without any need of coupling optics as light source. This simplifies the setup and provides sufficient magnification to measure microstructures. We evaluate our setup by imaging reflective microstructures. We have achieved ̴ 6 mm2 field of view amplitude images with ̴ 2.5μm lateral resolution and phase images with axial resolution in nanometer range. The phase image provides a full-field profile measurement of the sample in nanometer range.
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