The extraction of useful cues for object identification and navigation through visual scenes is technically challenging. Traditionally designed artificial systems struggle to solve this task in real time, despite utilising high-resolution cameras, sophisticated software and computers with hundreds of millions of transistors. However insects, with low-resolution eyes and small brains (less than a million neurons), are able to avoid obstacles and successfully navigate through complex surrounds during high-speed flight. By studying the underlying neuronal processes governing this remarkable ability it has been possible to reverse engineer models for biological visual processing which rival insects in image normalisation (ability to detect objects independent of environmental conditions) and fast, reliable motion detection across different scenes. These models have been implemented in both software simulations and real-world hardware.