Super-Resolution of single image using wavelets and interpolation based hybrid technique

Abstract

Super-resolution is a technique of producing a high-resolution (HR) image from one or more low-resolution (LR) images. Classical interpolation based magnification techniques like nearest-neighbor, bilinear and bicubic interpolation results in a larger image along with undesirable artifacts like blurring, aliasing and ringing effects. So the aim of superresolution is to provide a larger image with good quality (quality means an image with less undesirable artifacts). Previous super-resolution techniques are based on using multiple images and learning based methods but the idea here is to use a single image in the superresolution process. In this thesis combination of wavelet transform and interpolation based technique to achieve the super-resolution of a single image. First the edges of the image are boosted using wavelet transform. After boosting the edges the resultant image undergoes the process of magnification which is achieved using an interpolation based method. Interpolation based magnification algorithm produces high-resolution images that is free of undesirable artifacts. A comparison of this algorithm with other techniques proposed by other authors is also done to provide the quantitative and qualitative result to prove the effectiveness of the methods. For this purpose 85 test images were taken that belonged to different image categories, on which different super-resolution techniques were applied to access the effectiveness of the algorithm. In quantitative analysis, the quality measures used are correlation coefficient, mean-squared error and peak signal-to-noise ratio. The values of these measures suggested that the proposed algorithm produces good results. One of the conclusion derived during the analysis of algorithms results is that proposed algorithm cannot be applied to all categories of images. This technique is successful on those images that has less edges e.g. for crowd and satellite images, this technique of super-resolution is not appropriate. But this technique produces superior results for those images that have fewer edges e.g. face and object images.