NDLI: Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting

Content Provider	Springer Nature Link
Author	Chouhan, Rajlaxmi Biswas, P. K. Jha, R. K.
Copyright Year	2015
Abstract	This paper presents a study of noise-enhanced iterative processing on Fourier coefficients for enhancement of low-contrast images. The processing equation is derived from the concept of dynamic stochastic resonance (SR), where the presence of optimum amount of noise produces an improved performance in the system. Similar to our earlier works on SR-based contrast enhancement, noise in the current context is the internal noise inherent in an image due to insufficient illumination. Here, however, the parameter selection is done so as to achieve large noise suppression. Iteration is terminated when target performance has been achieved. It is observed that the increase in the variance of the Fourier magnitude distribution leads to an increase in the contrast of the image. The increase in the variance is analytically proven to be equivalent to the process of coefficient rooting. Comparison has been made with various state-of-the-art SR and non-SR-based techniques in spatial/frequency domains. The proposed technique has been found to give noteworthy performance for both low-contrast and dark images among the SR-based techniques. The performance is also found to be better than most of the non-SR-based techniques, in terms of contrast enhancement, perceptual quality and colorfulness.
Starting Page	255
Ending Page	263
Page Count	9
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	9
Issue Number	1
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2015-09-10
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Contrast enhancement Dynamic stochastic resonance Discrete Fourier transform Noise-enhanced image processing Signal, Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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