An Adaptive Image Zooming Algorithm Using Interpolation Techniques

Abstract

Image Zooming is an important task used in many applications. It is the process of enlarging the image to any factor of magnification. While zooming an image, there are few parameters that we have to keep in mind. Applying function indiscriminately to an image, to resample it, will generally result in aliasing; edge blurring and other artifacts may arise. So the main focus is on the reduction of these artifacts. This thesis considers various interpolation schemes, particularly focusing on adaptive methods because of their inherent abilities to preserve sharp edges and detail. Proposed algorithm is an adaptive resampling algorithm for zooming up images. The algorithm is based on analyzing the local structure of the image and applying a near optimal and least time-consuming resampling function will preserve edge locations and their contrast. This is done by segmenting the image dynamically into homogeneous areas, as it is scanned or received zooming algorithm which focuses on preserving edges. The algorithm reduces the jagging, blurring. To compare existing algorithms with proposed method algorithm, we have taken real world images and results are compared. And we have come to the decision that proposed algorithm is better than the existing algorithms. We have compared the images by two ways – Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).