The Remarkable Visual Abilities of Nocturnal Insects: Neural Principles and Bioinspired Night-Vision Algorithms

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, t...

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Bibliographic Details
Published in:	Proceedings of the IEEE 2014-10, Vol.102 (10), p.1411-1426
Main Authors:	Warrant, Eric, Oskarsson, Magnus, Malm, Henrik
Format:	Article
Language:	eng
Subjects:	Algorithms Biologi Biological Sciences Coarsening Compound eye Compounds Computer and Information Science Computer Vision and Robotics (Autonomous Systems) Data- och informationsvetenskap (Datateknik) Datorseende och robotik (autonoma system) Denoising Dung Eyes image enhancement Image processing insect Insects Matematik Mathematics Moths Natural Sciences Naturvetenskap nocturnal vision Noise measurement Photonics Photoreceptors structure tensor summation Vision Visual Visualization
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Summary:	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.
ISSN:	0018-9219 1558-2256