A Performance Impact of An Edge Kernel for The High-Frequency Image Prediction Reconstruction
A Performance Impact of An Edge Kernel for The High-Frequency Image Prediction Reconstruction
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2014
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eng
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application/pdf
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5 pages
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Proceeding, ISCIT 2015, u-lncheon Forum/Incheon Smart City Association and Inha University, Incheon, Korea, Sep. 2014. (IEEE)
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Abstract
As a rule, the performance of almost digital image
processing (DIP) algorithms and these applications directly
depends on the spatial resolution of observed input images.
Unfortunately, from the current image sensor technology, it is
hard to take sufficient high spatial resolution images from
commercial devices therefore the fantastic research attempts and,
consequently, simple digital image resolution enhancements have
been boosted in the last decade. The high-frequency image
prediction reconstruction is the simple and effective algorithm
for enhancing the image resolution however this algorithm is
strongly depends on the edge detection kernel and M0 parameter.
Therefore, this paper studies a performance impact of an edge
detection kernel such as Roberts kernel, Prewitt Kernel, Sobel
Kernel, Laplacian Kernel and Laplacian of Gaussian (LOG)
Kernel for the high-frequency image prediction reconstruction.
This paper presents three experimental performance studies
under a noiseless environment, several blurred environments at
different blurred variance and several noisy environments at
different noise power levels. The first performance study is an
empirical exhaustive study of an optimal edge detection kernel
and the study of optimal M0 value is experimentally determined
under this environment. The second performance study is an
empirical exhaustive study of an optimal edge detection kernel
and the study of optimal M0 value is experimentally determined
under these environments. Finally, the last performance study is
an empirical exhaustive study of an optimal edge detection kernel
and the study of optimal M0 value is experimentally determined
under these environments.