At night, our visual capacities are severely reduced, with a complete loss in our ability to see colour and a dramatic loss in our ability to see fine spatial and temporal details. This is not the case for many nocturnal animals, notably insects. Our recent work, particularly on fast-flying moths and bees and on ball-rolling dung beetles, has shown that nocturnal animals are able to distinguish colours, to detect faint movements, to learn visual landmarks, to orient to the faint pattern of polarised light produced by the moon and to navigate using the stars. These impressive visual abilities are the result of exquisitely adapted eyes and visual systems, the product of millions of years of evolution. Nocturnal animals typically have highly sensitive eye designs and visual neural circuitry that is optimised for extracting reliable information from dim and noisy visual images. Even though we are only at the threshold of understanding the neural mechanisms responsible for reliable nocturnal vision, growing evidence suggests that the neural summation of photons in space and time is critically important: even though vision in dim light becomes necessarily coarser and slower, it also becomes significantly more reliable. We explored the benefits of spatiotemporal summation by creating a computer algorithm that mimicked nocturnal visual processing strategies. This algorithm dramatically increased the reliability of video collected in dim light, including the preservation of colour, strengthening evidence that summation strategies are essential for nocturnal vision.