Mrs. G. Padma Priya Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 6, Issue 2, (Part - 3) February 2016, pp.93-96 www.ijera.com 93|Page Image Resolution Enhancement using DWT and Spatial Domain Interpolation Technique Mrs. G. Padma Priya*, Prof. T. Venkateswarlu** *(Research Scholar, Department of ECE, SVUCE, SV University, Tirupati.) ** (Professor, Department of ECE, SVUCE, SV University, Tirupati.) ABSTRACT Image Resolution is one of the important quality metrics of images. Images with high resolution are required in many fields. In this paper, a new resolution enhancement technique is proposed based on the interpolation of four sub band images generated by Discrete Wavelet Transform (DWT) and the original Low Resolution (LR) input image. In this technique, the four sub band images generated by DWT and the input LR image are interpolated with scaling factor, α and then performed inverse DWT to obtain the intermediate High Resolution (HR) Image. The difference between the intermediate HR image and the interpolated LR input image is added to the intermediate HR image to obtain final output HR Image. Lanczos interpolation is used in this technique. The proposed technique is tested on well known bench mark images. The quantitative and visual results shows the superiority of the proposed technique over the conventional and state of art image resolution enhancement techniques in wavelet domain using haar wavelet filter. Keywords: DWT, Lanczos Interpolation, Resolution. I. INTRODUCTION Image Resolution is one of the most important quality metrics of images and videos. Images with higher resolution are required in most of the imaging applications, such as, medical imaging, video standard conversion, remote sensing and surveillance video. Resolution of an image stands for number of pixels in image. Image with more number of pixels has high resolution. The pixel resolution can be specified with the set of two positive integer numbers, where the first number is the number of pixel columns (width) and the second is the number of pixel rows (height), for example as 512 x 512. The most widely used technique for enhancing the image resolution is Interpolation. Fundamentally, Interpolation is the process of using known data to estimate values at unknown locations [1]. In Image processing, Interpolation is a method to increase the number of pixels in digital image. Conventional Interpolation Techniques which are commonly used are Nearest Neighbor, Bilinear, Bicubic and Lanczos. Resolution Enhancement techniques which are not based on wavelets suffer from the drawback of losing high frequency contents which results in blurring of the images [2]. Recently some techniques have been proposed [2]-[7] in wavelet domain for resolution enhancement. Using Wavelet Transform, spectrum can be obtained as a function of shift and scale. Hence, it is suitable for obtaining spatial as well as spectral resolution enhancement. By using DWT, a HR Image can be decomposed into a LR Image and three wavelet detail images with horizontal, vertical and diagonal edge information at each scale by applying the 1D - DWT along the rows of the image first, and then the 1D - DWT along the column of the image. These four sub band images are referred to as LL, LH, HL, HH sub bands. The frequency components of these sub bands cover full frequency spectrum of the original image. Inverse DWT is used to obtain the original image using these four sub bands. The block diagram, representing the 2D – DWT process was given in Fig.1 and the corresponding output images for single level decomposition was given in Fig.2. (a) (b) Fig. 1: (a) Single level decomposition of 2D DWT (b) Single level 2D Inverse DWT. RESEARCH ARTICLE OPEN ACCESS