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Resolution enhancement of digital images with data‐dependent biasing
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AbstractIn the information and communications field, digital images have come to be widely used in mobile communications devices, and digital image processing is very important. Because of the different resolutions of the displays and output devices in each terminal, digital images undergo reduction and enlargement processing very frequently. Conventionally, reduction and enlargement processing is performed using an interpolation algorithm based on the sinc function. However, because the frequency band increases when digital images are enlarged, an enlarged image of sufficient quality cannot be attained through interpolation. In recent years, an enlargement method has been proposed which uses estimation of the frequency bands (high‐frequency components) which increase with the enlargement of a digital image. The authors have also clarified interpolation algorithms using the preservation of step edge signals, coordinate point warping for generating peak signals, and signal amplitude biasing. They have implemented an interpolation algorithm which produces high‐frequency components. In calculating the bias in this method, the smaller of the biases found to either side of the interpolation is selected. This choice is not necessarily optimal; the larger bias should be selected depending on certain conditions. For example, the selection of the larger bias makes it possible to preserve the precipitous changes in a step edge signal. This makes it possible to preserve step edge signals and generate peaks, without also using coordinate point warping, by selecting the optimal bias according to local information. The proposed interpolation algorithm using data‐dependent biasing was compared to an interpolation algorithm using coordinate point warping and signal amplitude biasing. The proposed algorithm was found to have a similar performance and clearly shown to be a method with low computational complexity. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(8): 18–28, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20306
Title: Resolution enhancement of digital images with data‐dependent biasing
Description:
AbstractIn the information and communications field, digital images have come to be widely used in mobile communications devices, and digital image processing is very important.
Because of the different resolutions of the displays and output devices in each terminal, digital images undergo reduction and enlargement processing very frequently.
Conventionally, reduction and enlargement processing is performed using an interpolation algorithm based on the sinc function.
However, because the frequency band increases when digital images are enlarged, an enlarged image of sufficient quality cannot be attained through interpolation.
In recent years, an enlargement method has been proposed which uses estimation of the frequency bands (high‐frequency components) which increase with the enlargement of a digital image.
The authors have also clarified interpolation algorithms using the preservation of step edge signals, coordinate point warping for generating peak signals, and signal amplitude biasing.
They have implemented an interpolation algorithm which produces high‐frequency components.
In calculating the bias in this method, the smaller of the biases found to either side of the interpolation is selected.
This choice is not necessarily optimal; the larger bias should be selected depending on certain conditions.
For example, the selection of the larger bias makes it possible to preserve the precipitous changes in a step edge signal.
This makes it possible to preserve step edge signals and generate peaks, without also using coordinate point warping, by selecting the optimal bias according to local information.
The proposed interpolation algorithm using data‐dependent biasing was compared to an interpolation algorithm using coordinate point warping and signal amplitude biasing.
The proposed algorithm was found to have a similar performance and clearly shown to be a method with low computational complexity.
© 2007 Wiley Periodicals, Inc.
Electron Comm Jpn Pt 3, 90(8): 18–28, 2007; Published online in Wiley InterScience (www.
interscience.
wiley.
com).
DOI 10.
1002/ecjc.
20306.
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