NDLI: Adaptive enhancement and noise reduction in very low light-level video

Content Provider	IEEE Xplore Digital Library
Author	Malm, H. Oskarsson, M. Warrant, E. Clarberg, P. Hasselgren, J. Lejdfors, C.
Copyright Year	2007
Description	Author affiliation: Lund Univ., Lund (Malm, H.; Oskarsson, M.; Warrant, E.)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	322989
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4409007
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Image sequences Animals Dynamic range Smoothing methods Kernel Color Image restoration Concurrent computing Noise level
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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Enhancement and noise reduction of very low light level images

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