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Image magnification is among the basic image processing operations and has many applications in a various area. In recent, multimedia techniques have advanced to provide various multimedia data that were digital images and VOD. It has been rapidly growing into a digital multimedia contents market. In education, many researches have used e-learning techniques. Various equipments - image equipments, CCD camera, digital camera and cellular phone – are used in making multimedia contents. They are now widespread and as a result, computer users can buy them and acquire many digital images as desired. This is why the need to display and print them also increases (Battiato & Mancuso, 2001; Battiato et al., 2002). However, such various images with optical industry lenses are used to get high-resolution. These lenses are not only expensive but also too big for us to carry. So, they are using the digital zooming method with the lenses to solve the problem. The digital zooming method generally uses the nearest neighbor interpolation method, which is simpler and faster than other methods. But it has drawbacks such as blocking phenomenon when an image is enlarged. Also, to improve the drawbacks, there exist bilinear interpolation method and the cubic convolution interpolation commercially used in the software market. The bilinear method uses the average of 4 neighborhood pixels. It can solve the blocking phenomenon but brings loss of the image like blurring phenomenon when the image is enlarged. Cubic convolution interpolation improved the loss of image like the nearest neighbor interpolation and bilinear interpolation. But it is slow as it uses the offset of 16 neighborhood pixels (Aoyama & Ishii, 1993; Candocia & Principe, 1999; Biancardi et al., 2002). A number of methods for magnifying images have been proposed to solve such problems. However, proposed methods on magnifying images have the disadvantage that either the sharpness of the edges cannot be preserved or that some highly visible artifacts are produced in the magnified image. Although previous methods show a high performance in special environment, there are still the basic problems left behind. Recently, researches on Human vision processing have been in the rapid progress. In addition, a large number of models for modeling human vision system have been proposed to solve the drawbacks of

Image Magnification Based on the Human Visual Processing