https://www.selleckchem.com/products/skf96365.html High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system's complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique combines many images of the sample, recorded at differing illumination angles akin to computed tomography and uses error minimisation between the recorded images with those generated by a forward model. The more precise knowledge of those illumination angles is available for the image formation forward model, the better the result. Therefore, illumination estimation from the raw data is an important step and supports correct phase recovery and aberration correction. Here, we derive how illumination estimation can be cast as an object detection problem that permits the use of a fast convolutional neural network (CNN) for this task. We find that faster-RCNN delivers highly robust results and outperforms classical approaches by far with an up to 3-fold reduction in estimation errors. Intriguingly, we find that conventionally beneficial smoothing and filtering of raw data is counterproductive in this type of application. We present a detailed analysis of the network's performance and provide all our developed software openly.We investigated the switching dynamics of optical modulators consisting of a Si waveguide with a VO2 cladding layer by utilizing the photothermal effect, which induces a metal-insulator transition in VO2. The devices exhibited stable optical switching with a high extinction ratio exceeding 16 dB. The switching time of the insulator-to-metal transition (heating process) ranged from tens of nanoseconds to microseconds depending on the incident light power, and that of the metal-to-insulator transition (cooli