Despite their tiny eyes and brains, nocturnal insects have remarkable visual abilities. Recent work - particularly on fast-flying moths and bees and on ball-rolling dung beetles - has shown that nocturnal insects are able to distinguish colors, to detect faint movements, to learn visual landmarks, to orient to the faint pattern of polarized 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. 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, those details that are preserved are seen clearly. These benefits of spatio-temporal summation have obvious implications for dim-light video technologies. In addition to reviewing the visual adaptations of nocturnal insects, we here describe an algorithm inspired by nocturnal visual processing strategies - from amplification of primary image signals to optimized spatio-temporal summation to reduce noise - that dramatically increases the reliability of video collected in dim light, including the preservation of color.

A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial detail and prevent motion blur. In color image data, the chromaticity is restored and demosaicing of raw RGB input data is performed simultaneously with the noise reduction. The method is very general, contains few user-defined parameters and has been developed for efficient parallel computation using a GPU. The technique has been applied to image sequences with various degrees of darkness and noise levels, and results from some of these tests, and comparisons to other methods, are presented. The present work has been inspired by research on vision in nocturnal animals, particularly the spatial and temporal visual summation that allows these animals to see in dim light.

In a previous publication (R.M. Olberg, 1986), the anatomical identification and visual physiology of eight large interneurons descending in the dragonfly ventral nerve cord from the brain to the thoracic ganglia was described. These cells are strictly visual and respond only to movements of small patterns, such as dark squares on a white background, and are inhibited by large visual patterns such as gratings, or even small patterns slightly enlarged from the preferred size. To examine inhibitory circuit hypotheses for these visual interneurons, the authors measured the cells' preferred small object size as a function of mean background luminance. Based on the results they propose a shunting inhibition model with antecedent multipliers.>