Title :
The Remarkable Visual Abilities of Nocturnal Insects: Neural Principles and Bioinspired Night-Vision Algorithms
Author :
Warrant, Eric ; Oskarsson, Magnus ; Malm, Henrik
Author_Institution :
Dept. of Biol., Lund Univ., Lund, Sweden
Abstract :
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.
Keywords :
biomimetics; colour vision; computer vision; eye; night vision; video signal processing; zoology; bees; bioinspired night vision algorithms; color vision; dim light environments; dim light video technologies; dung beetles; faint movement detection; moths; neural photon summation; nocturnal insect visual abilities; nocturnal insect visual adaptations; nocturnal visual processing strategies; polarized light; primary image signal amplification; spatiotemporal summation; visual landmark learning; Compounds; Denoising; Image processing; Insects; Noise measurement; Photonics; Photoreceptors; Visualization; Compound eye; denoising; image enhancement; insect; nocturnal vision; structure tensor; summation;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2014.2332533