Image Resolution Enhancement Method Based on Feature Space

Abstract

In this paper, an approach for resolution enhancement of color image based on feature space method is proposed. Many images, such as landscape, natural scene and so on, are fractal, and they can often be assumed as self-correlation. Focusing on this point, we describe the image by feature space. This method reflects the characteristics of color image, and realizes the image interpolation by using the feature space. Experiment result shows that this method can enhance the image resolution more effectively comparing with nearest neighbor interpolation, bilinear int erpolation. In troduction Image resolution enhancement is a method of signal processing and image processing. It can transform an existing low resolution image into high resolution image by using the software algorithm. In video monitoring, image printing, forensic analysis, medical image processing, satellite imaging and other fields, image resolution enhancement has been applied widely. Image resolution enhancement related to some basic problem in many fields such as image processing, computer vision, optimization theory. Resolution enhancement has become one of the hot spots in the research field and it has important significance to image processing (1). Image resolution enhancement is an image data regeneration process within a given range of space. The more image data can be estimated by the finite discrete image data accurately to construct observation images with higher resolution to reflect the real scene. Advanced resolution enhancement technique can effectively enhance the image resolution, so it is widely used in satellite remote sensing, material analysis, medical diagnostic, traffic management, criminal investigation and so on. R esolution enhancement is divided into the traditional interpolation methods and modern interpolation method. The traditional interpolation method is also known as linear interpolation that uses the known image pixel gray value and special interpolation function to calculate the unknown pixel gray value. The typical linear interpolation methods are nearest neighbor interpolation, bilinear int erpolation, bicubic interpolation, polynomial interpolation and spline interpolation algorithm (2-6). The high frequency information of the image is inhibited by traditional interpolation method, so the blur or aliasing phenomenon would appear in the edge region of restoration images, and it is not ideal to restore the edge and texture features of the image. The modern interpolation method is also called nonlinear interpolation, and it int roduces the fractal topology, wavelet analysis, partial differential equations, nonlinear optimization theory etc. into digital image processing (7-10). The modern int erpolation methods can use different interpolation method in different regions of image according to t he regulation of the image spectrum weighted coefficient. In this paper, the local images are cut out form the color image, such as landscape image, texture im age, building image and so on. From these local images, a similar pattern can be extracted by a specific feature space, and the pattern can express the image compactly. Using this pattern we propose a method to enhance the image resolution.