An Experimental Performance Analysis of Image Reconstruction Techniques under Both Gaussian and Non-Gaussian Noise Models
An Experimental Performance Analysis of Image Reconstruction Techniques under Both Gaussian and Non-Gaussian Noise Models
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2012-07
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eng
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8 pages
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Proceedings of the 4th International Conference on Knowledge and Smart Technologies (KST-2012), Burapha University, Chonburi, Thailand, July 7-8, 2012. (IEEE Xplore)
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Abstract
Recently, the images reconstruction approaches are
very essential in digital image processing (DIP), especially in
terms of removing the noise contaminations and recovering the
content of images. Each image reconstruction approach has
different mathematical models. Therefore a performance of
individual reconstruction approach is varied depending on
several factors such as image characteristic, reconstruction
mathematical model, noise model and noise intensity. Thus, this
paper presents comprehensive experiments based on the
comparisons of various reconstruction approaches under
Gaussian and non-Gaussian noise models. The employing
reconstruction approaches in this experiment are Inverse Filter,
Wiener Filter, Regularized approach, Lucy-Richardson (L-R)
approach, and Bayesian approach applied on mean, median,
myriad, meridian filters together with several regularization
techniques (such as non-regularization, Laplacian regularized,
Markov Random Field (MRF) regularization, and one-side Bi-
Total Variation (OS-BTV) regularization). Three standard
images of Lena, Resolution Chart, and Susie (40th) are used for
testing in this experiment. Noise models of Additive White
Gaussian Noise (AWGN), Poisson, Salt&Pepper, and Speckle of
various intensities are used to contaminate all these images. The
comparison is done by varying the parameters of each approach
until the best peak-signal-to-noise ratio (PSNR) is obtained.
Therefore, PSNR plays a vital parameter for comparisons all the
results of individual approaches.