Stochastically structured illumination microscopy scan less super resolution imaging.

In super-resolution, a varying illumination image stack is required. This enriched dataset typically necessitates precise mechanical control and micron-scale optical alignment and repeatability. Here, we introduce a novel methodology for super-resolution microscopy called stochastically structured illumination microscopy (SIM), which bypasses the need for illumination control exploiting instead the random, uncontrolled movement of the target object.

Physics-informed deep neural network for image denoising.

Image enhancement deep neural networks (DNN) can improve signal to noise ratio or resolution of optically collected visual information. The literature reports a variety of approaches with varying effectiveness. All these algorithms rely on arbitrary data (the pixels' count-rate) normalization, making their performance strngly affected by dataset or user-specific data pre-manipulation.

Deep learning for blind structured illumination microscopy.

Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns. It is thus advantageous in experimental conditions where toxicity or biological fluctuations are an issue. In this work, we introduce a custom convolutional neural network architecture for blind-SIM: BS-CNN.

Weakly-coupled quasi-1D helical modes in disordered 3D topological insulator quantum wires.

Disorder remains a key limitation in the search for robust signatures of topological superconductivity in condensed matter. Whereas clean semiconducting quantum wires gave promising results discussed in terms of Majorana bound states, disorder makes the interpretation more complex. Quantum wires of 3D topological insulators offer a serious alternative due to their perfectly-transmitted mode.