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Biologically inspired enhancement of dim light video

Malm, Henrik LU ; Oskarsson, Magnus LU and Warrant, Eric LU (2012) In Frontiers in Sensing: From Biology to Engineering p.71-85
Abstract
In this chapter a technology for the enhancement of video data obtained at low light levels is presented. The method was inspired by the way in which nocturnal animals adaptively sum intensities, spatially and temporally, to improve vision at night. Due to the low photon count under these conditions the visual input is dark and unreliable, which leads to noisy low contrast images. The noise becomes very apparent when we try to enhance the contrast and, by this, amplify the intensities in the darkest regions of the images. By constructing spatio-temporal smoothing kernels that automatically adapt to the three dimensional intensity structure at every point, the noise can be considerably reduced, with fine spatial detail being preserved and... (More)
In this chapter a technology for the enhancement of video data obtained at low light levels is presented. The method was inspired by the way in which nocturnal animals adaptively sum intensities, spatially and temporally, to improve vision at night. Due to the low photon count under these conditions the visual input is dark and unreliable, which leads to noisy low contrast images. The noise becomes very apparent when we try to enhance the contrast and, by this, amplify the intensities in the darkest regions of the images. By constructing spatio-temporal smoothing kernels that automatically adapt to the three dimensional intensity structure at every point, the noise can be considerably reduced, with fine spatial detail being preserved and enhanced without added motion blur. For color image data, the chromaticity is restored and demosaicing of raw RGB input data can be performed simultaneously with the noise reduction. The method is a very generally applicable one, contains only few user-defined parameters and has been developed for efficient parallel computation using a graphics processing unit (GPU). The technique has been applied to image sequences with various degrees of darkness and noise levels. Results from some of these tests, and comparisons to related work, are presented here. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Frontiers in Sensing: From Biology to Engineering
editor
Barth, Friedrich G.; Humphrey, Joseph A. C. and Srinivasan, Mandyam V.
pages
71 - 85
publisher
Springer
external identifiers
  • Scopus:84930018492
ISBN
978-3-211-99749-9
978-3-211-99748-2 (Print)
DOI
10.1007/978-3-211-99749-9_5http://dx.doi.org/10.1007/978-3-211-99749-9
language
English
LU publication?
yes
id
310769b2-b76a-4084-9c24-eb5463870b25 (old id 5052476)
date added to LUP
2011-12-22 13:32:41
date last changed
2016-10-13 04:44:15
@misc{310769b2-b76a-4084-9c24-eb5463870b25,
  abstract     = {In this chapter a technology for the enhancement of video data obtained at low light levels is presented. The method was inspired by the way in which nocturnal animals adaptively sum intensities, spatially and temporally, to improve vision at night. Due to the low photon count under these conditions the visual input is dark and unreliable, which leads to noisy low contrast images. The noise becomes very apparent when we try to enhance the contrast and, by this, amplify the intensities in the darkest regions of the images. By constructing spatio-temporal smoothing kernels that automatically adapt to the three dimensional intensity structure at every point, the noise can be considerably reduced, with fine spatial detail being preserved and enhanced without added motion blur. For color image data, the chromaticity is restored and demosaicing of raw RGB input data can be performed simultaneously with the noise reduction. The method is a very generally applicable one, contains only few user-defined parameters and has been developed for efficient parallel computation using a graphics processing unit (GPU). The technique has been applied to image sequences with various degrees of darkness and noise levels. Results from some of these tests, and comparisons to related work, are presented here.},
  author       = {Malm, Henrik and Oskarsson, Magnus and Warrant, Eric},
  editor       = {Barth, Friedrich G. and Humphrey, Joseph A. C. and Srinivasan, Mandyam V.},
  isbn         = {978-3-211-99749-9},
  language     = {eng},
  pages        = {71--85},
  publisher    = {ARRAY(0x8f45618)},
  series       = {Frontiers in Sensing: From Biology to Engineering},
  title        = {Biologically inspired enhancement of dim light video},
  url          = {http://dx.doi.org/10.1007/978-3-211-99749-9_5http://dx.doi.org/10.1007/978-3-211-99749-9},
  year         = {2012},
}