Image Magnification Using Adaptive Interpolation by Pixel Level Data-Dependent Geometrical Shapes Muhammad Sajjad, Naveed Khattak, and Noman Jafri Abstract—World has entered in 21 st century. The technology of computer graphics and digital cameras is prevalent. High resolution display and printer are available. Therefore high resolution images are needed in order to produce high quality display images and high quality prints. However, since high resolution images are not usually provided, there is a need to magnify the original images. One common difficulty in the previous magnification techniques is that of preserving details, i.e. edges and at the same time smoothing the data for not introducing the spurious artefacts. A definitive solution to this is still an open issue. In this paper an image magnification using adaptive interpolation by pixel level data-dependent geometrical shapes is proposed that tries to take into account information about the edges (sharp luminance variations) and smoothness of the image. It calculate threshold, classify interpolation region in the form of geometrical shapes and then assign suitable values inside interpolation region to the undefined pixels while preserving the sharp luminance variations and smoothness at the same time. The results of proposed technique has been compared qualitatively and quantitatively with five other techniques. In which the qualitative results show that the proposed method beats completely the Nearest Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The quantitative results are competitive and consistent with NN, BL, BC and others. Keywords—Adaptive, digital image processing, image magnification, interpolation, geometrical shapes, qualitative & quantitative analysis. I. INTRODUCTION ODAY, there is a huge amount of digital images available to computer users. This is caused by the rapid growth both in computer hardware and software technologies. Low price digital cameras are now common, and as a result users are able to buy them and take as many digital images as desired. The significant development in the field of computer graphics has also boosted the production of digital images. As computer users become more familiar with digital images, the need to display and print them also increases. In an era where high- resolution display and printing devices are common, it is vital that high-resolution images are available in order to produce high quality displayed images and high quality prints. This is particularly important for desktop publishing, large artistic printing, etc. The problem is that high-resolution images are not usually provided. In these cases, there is a need to Authors are with Department of computer Science, College of signals, National University of Sciences and Technology, Rawalpindi, Pakistan (e- mail: qazi.msajjad@gmail.com, {khattakn, mnjafri}@mcs.edu.pk). magnify the original images. Therefore, the development of a good image magnification algorithm is very important. Until now, a large number of interpolation techniques for magnifying images have been proposed. A typical problem with most interpolation techniques is that although smoothing the data and keeping the low frequencies in the new zoomed picture, they are not able to enhance the high frequencies or preserve the edges equally well. Visually those problems will result in either blurring or blocking artifacts. A possible solution would need a sort of non-linear interpolation, taking into account the directional variation for maintaining the sharpness of the new enlarged image and smoothness as well. The simplest method to magnify images is the pixel replication. However, the resulting magnified images have aliasing effect in the form of jagged edges. Nearest neighbor interpolation is the simplest method and basically makes the pixels bigger. The color of a pixel in the new image is the color of the nearest pixel of the original image[4]. Most image viewing and editing software use this type of interpolation to enlarge a digital image for the purpose of closer examination because it does not change the color information of the image and does not introduce any anti-aliasing. For the same reason, it is not suitable to enlarge photographic images because it increases the visibility of jaggies. More elaborate approaches use the bilinear or the bicubic interpolation. Bilinear Interpolation determines the value of a new pixel based on a weighted average of the 4 pixels in the nearest 2 x 2 neighborhood of the pixel in the original image [4]. The averaging has an anti-aliasing effect and therefore produces relatively smooth edges with hardly any jaggies. Bicubic interpolation is more sophisticated and produces smoother edges than bilinear interpolation. Here, a new pixel is a bicubic function using 16 pixels in the nearest 4 x 4 neighborhood of the pixel in the original image [1, 4]. This is the method most commonly used by image editing software, printer drivers and many digital cameras for resampling images. Commercial software Adobe Photoshop [1] provides these two functions for interpolating images. Other methods, using the B-spline interpolators [8, 11] or the cubic convolution methods [10] have also been proposed. However, these methods tend to blur the edges and cause them to be jagged. Research on interpolating images taking into account the edges, has gained much attention. Allebach and Wong [2] proposed methods that search for edges in the input image and use them to assure that the interpolation does not cross T World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:1, No:7, 2007 1906 International Scholarly and Scientific Research & Innovation 1(7) 2007 scholar.waset.org/1307-6892/5210 International Science Index, Computer and Information Engineering Vol:1, No:7, 2007 waset.org/Publication/5210