NDLI: Pixel classification algorithms for noise removal and signal preservation in low-pass filtering for contrast enhancement

Content Provider	IEEE Xplore Digital Library
Author	Chunyan Wang Sha Gong
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada (Chunyan Wang; Sha Gong)
Abstract	With a view to obtaining a high quality contrast enhancement, low-pass filters are used to remove the noise generated in a high-gain histogram equalization process. To preserve signal variations, the LP operation applied to the pixels in non-homogeneous regions should have less smoothing strength than that in homogeneous regions. The pixel classification according to the gray level homogeneity is thus a critical part in the LP filtering. In this paper, two algorithms for pixel classification according to the gray level homogeneity of their regions are proposed. In each of them, image pixels are grouped in such a way that, in the same group, pixels in homogeneous regions can be easily distinguished from those in non-homogeneous regions by a simple gradient thresholding, despite the complexity of signal gradient degradation in images. The two proposed classification algorithms are very simple, requiring very small quantity of computation. Their effectiveness has been proven by the simulation results.
Starting Page	480
Ending Page	485
File Size	1469218
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Classification algorithms Signal processing algorithms Noise Filtering Digital signal processing Algorithm design and analysis contrast enhancement Gray level homogeneity high dynamic range (HDR) noise removal signal preservation low-pass filtering high-pass filtering gradient detection
Content Type	Text
Resource Type	Article

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