arXiv subjects

Benedict Diederich

Publications and source records attributed to Benedict Diederich.

6 recordsLinked to original sources

arXiv2026

Advancing optical imaging systems with digital fabrication

Optical imaging technologies are central to discovery in the life and physical sciences, yet their impact depends on how readily they can be built, adapted, and sustained across laboratories. Digital fabrication, including desktop 3D printing, offers new ways to engineer imaging instruments by simplifying assembly, lowering replication barriers, and enabling modular integration and local refinement. Here we examine, using open microscopy as a transparent case, how digitally fabricated components support adaptable, research-grade optical systems while enabling faster innovation cycles and distributed refinement. We outline practical design guidelines and discuss emerging developments that may further advance accessible, high-performance imaging.

physics.bio-ph↗

arXiv2025

UC2-ESP: A General-Purpose Framework for Open-Source Microscopy Control

Building the optical setup for investigating biological questions comes with challenges. A major such challenge is setting up and synchronizing the control of multiple hardware components such as stages, cameras and lasers. With UC2-ESP we present a compact electronics system powered by the ESP32 microcontroller, designed to provide general-purpose control for various components in microscopy setups. Our system can interface with stepper motors, directed current (DC) motors, lasers (transistor-transistor logics, TTL or pulse width modulation, PWM), light emitting diodes (LEDs), and analog voltage outputs (galvo mirrors, led current control), allowing precise control over microscopy hardware. The platform is highly flexible, supporting custom pin configurations and multiple communication interfaces such as Bluetooth, universal serial bus (USB-serial), and HTTP via a built-in Webserver. A PlayStation controller can be used for haptic hardware manipulation, while commands are transmitted in a human-readable JSON format to ensure modularity and extensibility. The firmware is designed to receive parameters and execute actions dynamically, supporting complex control loops such as motor homing, stage scanning and temperature regulation via integrated controllers. Furthermore, the system integrates seamlessly with ImSwitch as well as MicroManager and offers a browser-based control tool using Web Serial. This open-source firmware enables microscopy research groups to develop custom setups and expand functionality efficiently, at low cost and high flexibility.

physics.optics↗

arXiv2021

Open microscopy in the life sciences: Quo Vadis?

Light microscopy allows observing cellular features and objects with sub-micrometer resolution. As such, light microscopy has been playing a fundamental role in the life sciences for more than a hundred years. Fueled by the availability of mass-produced electronics and hardware, publicly shared documentation and building instructions, open-source software, wide access to rapid prototyping and 3D printing, and the enthusiasm of contributors and users involved, the concept of open microscopy has been gaining incredible momentum, bringing new sophisticated tools to an expanding user base. Here, we will first discuss the ideas behind open science and open microscopy before highlighting recent projects and developments in open microscopy. We argue that the availability of well-designed open hardware and software solutions targeting broad user groups or even non-experts, will increasingly be relevant to cope with the increasing complexity of cutting-edge imaging technologies. We will then extensively discuss the current and future challenges of open microscopy.

q-bio.OT↗

arXiv2021

Super-resolution structured illumination microscopy: past, present and future

Structured illumination microscopy (SIM) has emerged as an essential technique for 3D and live-cell super-resolution imaging. However, to date, there has not been a dedicated workshop or journal issue covering the various aspects of SIM, from bespoke hardware and software development and the use of commercial instruments to biological applications. This special issue aims to recap recent developments as well as outline future trends. In addition to SIM, we cover related topics such as complementary super-resolution microscopy techniques, computational imaging, visualisation and image processing methods.

physics.optics↗

arXiv2018

cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM

Expensive scientific camera hardware is amongst the main cost factors in modern, high-performance microscopes. Recent technological advantages have, however, yielded consumer-grade camera devices that can provide surprisingly good performance. The camera sensors of smartphones in particular have benefited of this development. Combined with computing power and due to their ubiquity, smartphones provide a fantastic opportunity for "imaging on a budget". Here we show that a consumer cellphone is capable even of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we investigated an approach by a trained image-to-image generative adversarial network (GAN). This not only serves as a versatile technique to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance, but also allows processing directly on the smartphone. We believe that "cellSTORM" paves the way for affordable super-resolution microscopy suitable for research and education, expanding access to cutting edge research to a large community.

eess.IV↗

arXiv2017

Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.

physics.optics↗