Review of Different Schemes for Image Enlargement

In the development of modern information technology, image processing is becoming more and more important in our life. Digital zooming is encountered in many real applications such as electronic publishing ,image data base ,digital camera ,visible wireless telephone ,medical imaging, remote sensing and so on.In order to have better and fine images for users ,images are need to be reproduced to higher resolution from lower resolution. Image enlargement is done through interpolation technique which introduces many artifacts. Here new method is introduced for image zooming which is combination of conventional bilinear interpolation and error amender technique called EASE. Index Terms: Interpolation, Error Amender Sharp Edge detection (EASE), Mean Square Error (MSE), Signal to Noise Ratio (SNR), Root Mean Square Error (RMS) I. INTRODUCTIONDigital images are the most common and convenient means of conveying or transmitting information.They convey information about positions, sizes and inter-relationships between objects.Image enlargement or zooming is important in many aspects of today's digital world.Image enlargement is among the fundamental image processing operations.Typically zooming is related to scaling up visuals or images to be able to see more detail, increasing resolution, using optics, printing techniques, or digital processing.In all cases, the zooming of the image does not change the perspective of the image.Applications are varied in different fields.In medical imaging, zooming can serve to improve the chances of diagnosing problems by highlighting any possible aberrations.Enhancing image details can also be useful for the purposes of identification, whether for improving the quality of an image interpreted by a biometric recognition system or trying to get a clearer view of the perpetrator of some crime.In entertainment, zooming can be used to resize a video frame to fit the field of view of a projection device, which may help to reduce blurring.Finally, the most obvious application of image zooming is to simply allow one to enjoy a larger version of a favourite image obtained from any commercially available digital imaging device such as a camera, camcorder or scanner.For instance, high-resolution cameras are able to digitize scenes at a much finer scale and thus capture much more detail than lowerresolution cameras.Unfortunately, not all pictures and images can be stored at high resolution due to equipment, memory, and in the case of the Internet, bandwidth limitations.Consumers still need low-resolution images to be enlarged to higher resolution for viewing, printing, and editing, creating a need for interpolation algorithms that give end-users these magnified images.Image interpolation is a process that estimates a set of unknown pixels from a set of known pixels in an image.It has been widely used in a variety of applications such as image resizing, image zooming, image enhancement, image reduction, sub pixel image registration, image decomposition and to correct spatial distortions and many more.Many of the interpolation techniques like nearest neighbor, bicubic, bilinear and new edge directed interpolation [1] are available, which are discussed in section II.These techniques show many artifacts such as jaggies, blur, checker board effect and many more which are discussed in section III.Here we have proposed technique which will take digital image and enlargement factor as input and gives enlarged image with sharpened edges as image enlargement using error amender technique.This is discussed in section IV. II. INTERPOLATIONInterpolation is the process of defining a spatially continuous image from a set of discrete samples.Many of the interpolation techniques like nearest neighbor, bicubic, bilinear are available in many image processing tools like Photoshop.Various applications of interpolation are image resizing, image zooming, image enhancement, image reduction, sub pixel image registration, image decomposition and to correct spatial distortions and many more.Below figure shows the effect of interpolation on an image