NDLI: Use Fractal Brown Random theory to enhance infrared image

Content Provider	IEEE Xplore Digital Library
Author	Yu Tianhe Yu Xiaoyang Mao Yinhang Dai Jingmin
Copyright Year	2014
Description	Author affiliation: Dept. of Autom. Meas. & control Eng., Harbin Inst. of Technol., Harbin, China (Dai Jingmin) \|\| Coll. of Electr. & Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin, China (Yu Tianhe; Yu Xiaoyang; Mao Yinhang)
Abstract	Blurring edge of the infrared image is not conducive to human eye observation. In order to improve the visual effect of infrared image and the visibility of infrared image, the theory of fractal Brown is used to enhance infrared image in this paper. Fractal Brown Random(FBR) with the nature of random field shows that small area of the image meet the self-similarity, but the regularity of the boundary of the area is broken, therefore the occurrence of singular boundary value H, infrared image gray surface roughness is described. First calculating the fractal parameters of the image of each pixel, according to the visual sensitive characteristic of the human eyes to classify pixel of an image, which are classified as smooth points and details, and then the pixel are respective weighted enhance. According to the experiment we know that enhanced image highlighted the contour of an object which can obtain a good visual effect. Because the visual characteristic is fully considered in this method, the problem of poor visibility blurred edges of infrared images can be solved.
Starting Page	410
Ending Page	413
File Size	1873596
Page Count	4
File Format	PDF
e-ISBN	9781479969890
DOI	10.1109/WARTIA.2014.6976282
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Histograms Fractal Image edge detection HVS Fractals Surface roughness Rough surfaces Image enhancement Infrared image
Content Type	Text
Resource Type	Article

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