Image Magnification Based on the Properties of Human Visual Processing

Authors:
Sung-Kwan Je;Kwang-Baek Kim;Jin-Young Lee;Jae-Hyun Cho
Affiliations:
Dept. of Computer Science, Pusan National University,;Dept. of Computer Engineering, Silla University,;Dept. of Digital Design, Busan Digital University,;Dept. of Computer Engineering, Catholic University of Pusan,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

Citing 6
Cited 0

Scale-dependent reproduction of pen-and-ink illustrations

SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Digital Image Processing

Digital Image Processing
A Progressive Refinement Approach for Image Magnification

PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
New edge-directed interpolation

IEEE Transactions on Image Processing
Adaptively quadratic (AQua) image interpolation

IEEE Transactions on Image Processing
Super-resolution of images based on local correlations

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image magnification is among the basic image processing operations. The most commonly used techniques for image magnification are based on interpolation method. However, the magnified images produced by the techniques, such as nearest neighbor, bilinear and cubic method, often appear a variety of undesirable image artifacts such as 'blocking' and 'blurring' into the several processing for image magnification. In this paper, we propose image magnification method by properties of human visual system which reduce information during transforming from receptors to ganglion cells in retina and magnify information at visual cortex. Our method uses the whole image to exactly detect the edge information of the image and then emphasizes edge information. Experiment results show that the proposed method solves the drawbacks of the image magnification, such as blocking and blurring, and has a higher PSNR and Correlation than the traditional methods.