NDLI: A performance impact of an edge kernel for the high-frequency image prediction reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Patanavijit, Vorapoj Pirak, Chaiyod Ascheid, Gerd
Copyright Year	2014
Description	Author affiliation: Department of Electrical Engineering, Assumption University, Bangkok, Thailand 10240 (Patanavijit, Vorapoj) \|\| The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut's University of Technology North Bangkok (KMUTNB), Thailand (Pirak, Chaiyod) \|\| The Institute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Germany (Ascheid, Gerd)
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.
Starting Page	484
Ending Page	488
File Size	1843184
Page Count	5
File Format	PDF
ISBN	9781479944163
DOI	10.1109/ISCIT.2014.7011960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PSNR Laplace equations Image resolution Image edge detection Image Resolution Enhancement High-Frequency Image Prediction Prediction algorithms Digital Image Reconstruction Kernel Image reconstruction
Content Type	Text
Resource Type	Article

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