Adaptive enhancement and noise reduction in very low light-level video
Author
Summary, in English
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.
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.
Department/s
Publishing year
2007
Language
English
Pages
1395-1402
Publication/Series
11th International Conference on Computer Vision, 2007
Document type
Conference paper
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Computer Science
- Computer Vision and Robotics (Autonomous Systems)
Conference name
IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007
Conference date
2007-10-14 - 2007-10-21
Conference place
Rio de Janeiro, Brazil
Status
Published
ISBN/ISSN/Other
- ISSN: 1550-5499
- ISBN: 978-1-4244-1631-8