In 2012, I started working on Super-resolution Optical Fluctuation Imaging at SCIAN-Lab, Universidad de Chile, collaborating with researchers at the III Physics Institute of the University of Gottingen in Germany. The goal was to implement SOFI from the ground up — cumulant computation, synthetic quantum dot simulators, deconvolution — and apply it to biological samples. After months of work, we achieved the first successful SOFI super-resolution imaging in Chile, resolving structures below the diffraction limit using nothing more than temporal statistics from blinking fluorescent emitters.

From MATLAB folder to web app

The original MATLAB code did its job well. It produced results, contributed to a SPIE publication, and then sat quietly in a folder for over a decade. That is the natural life cycle of most research code. But recently, while modernizing several old projects, I decided to give the SOFI pipeline a proper second life as a Python web application with FastAPI and interactive visualization.

The core mathematics have not changed — cumulants are cumulants — but wrapping them in a clean interface with synthetic blinking simulations and real-time parameter adjustment makes the ideas far more accessible. A student can now open a browser, generate a quantum dot field, compute cumulants from order 2 to 6, and watch the resolution improve before their eyes. No MATLAB license, no installation, no archaeology through uncommented scripts.

There is something satisfying about seeing computational optics work from a decade ago running again and being immediately useful. The physics does not expire. Making it accessible through a browser gives it new life — not just for research reproducibility, but as a teaching and demonstration tool for anyone curious about what happens when you push imaging beyond the diffraction limit.