B Venkataramana, K Durga Gangarao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.1562-1565 1562 | P a g e Image Resolution Enhancement by Using Multi Resolution Transform-DWT B Venkataramana 1 , K Durga Gangarao 2 1, 2 (Department of ECE, University College of Engineering, JNTUK, AP. ABSTRACT In this paper we propose an image resolution enhancement technique based on the interpolation of high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate state to estimate the high frequency sub bands by utilizing the difference image obtained by subtracting the input image and its interpolated LL sub band. The proposed technique has been tested on well-known benchmark images. The quantitative (peak signal to noise ratio) and visual results show the superiority of the proposed technique over the conventional and state-of art image resolution enhancement techniques. Keywords - Discrete wavelet transform(DWT), image resolution enhancement, Interpolation, peak signal to noise ratio(PSNR). I. INTRODUCTION Image interpolation[5] occurs in all digital photos at some stage — whether this be in bayer demosaicing or in photo enlargement. It happens anytime you resize or remap (distort) your image from one pixel grid to another. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur under a wider variety of scenarios: correcting for lens distortion, changing perspective, and rotating an image. Even if the same image resize or remap is performed, the results can vary significantly depending on the interpolation algorithm. It is only an approximation, therefore an image will always lose some quality each time interpolation is performed. Common interpolation algorithms can be grouped into two categories: adaptive and non-adaptive. Adaptive methods change depending on what they are interpolating (sharp edges vs. smooth texture), whereas non-adaptive methods treat all pixels equally. Non- adaptive algorithms include: nearest neighbor, bilinear, bicubic, spline, sinc, lanczos and others. Depending on their complexity, these use anywhere from 0 to 255 (or more) adjacent pixels when interpolating. The more adjacent pixels they include, the more accurate they can become, but this comes at the expense of much longer processing time. These algorithms can be used to both distort and resize a photo. Adaptive algorithms include many proprietary algorithms in licensed software such as: Qimage, Photo Zoom Pro, Genuine Fractals and others. Many of these apply a different version of their algorithm (on a pixel-by-pixel basis) when they detect the presence of an edge — aiming to minimize unsightly interpolation artifacts in regions where they are most apparent. Basic interpolation techniques are described below. A. Nearest Neighbor Interpolation Nearest neighbor[8] is the most basic and requires the least processing time of all the interpolation algorithms because it only considers one pixel — the closest one to the interpolated point. This has the effect of simply making each pixel bigger. B. Bilinear Interpolation Bilinear interpolation[7][8] considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel. It then takes a weighted average of these 4 pixels to arrive at its final interpolated value. This results in much smoother looking images than nearest neighbor. C. Bicubic Interpolation Bicubic[7] goes one step beyond bilinear by considering the closest 4x4 neighborhood of known pixels — for a total of 16 pixels. Since these are at various distances from the unknown pixel, closer pixels are given a higher weighting in the calculation. Bicubic produces noticeably sharper images than the previous two methods, and is perhaps the ideal combination of processing time and output quality. Wavelets[4] are also playing a significant role in many image processing applications. Fig.1[7] shows the 2-D wavelet decomposition of an image is performed by applying the 1-D discrete wavelet transform (DWT)[1] along the rows of the image first, and then finally, corrected interpolated high frequency sub bands and interpolated input image are combined with the help of inverse DWT (IDWT) to achieve a high resolution output image. The results are decomposed into columns. This