Image resolution is always a key feature for all kinds of images. With ever increasing sizes of the displays need for super resolution images has also been increased. This is also impacted by the limited size of the digital image sensor. Resolution enhancement is always being associated with the interpolation techniques. Research suggests that interpolation methods increase the intensity of low frequency components. This means interpolated image will have less number of sharp intensity transactions per pixel. The process of super resolution is more important in the analysis of the identification of the important informations from the images more accurately. The enhancement of resolution of the images were employed based on the decomposition of the images. Basis functions of the WT are small waves located in different times. They are obtained using scaling and translation of a scaling function and wavelet function Therefore, the WT is localized in both time and frequency. The image decomposition is employed based on modified wavelet zero padding. The modified wavelet padding includes the usage of the two different wavelet decomposition methods (DWT and SWT). The wavelet transformation decomposes the images into different coefficients based on different wavelet filter banks. The high pass and the low pass filters were employed and based on filter results the coefficients were identified. The input image is first resized to a size of 256 x 256. The images were the subjected to decomposition based on both SWT and DWT. While applying DWT the original images were splitted into four coefficients. The coefficients were minimized in size compared to the input image size. While applying SWT the original images were splitted into four coefficients. The coefficients were of the same size compared to the input image size. The DWT coefficients were resized to the size of the SWT coefficients by using zero padding. The zero padding is employed by creating an empty matrix of the size of the SWT coefficients and placing the DWT coefficients in them. Inverse transformation is employed to the obtained modified coefficient and the remaining of the SWT coefficients. The resulting image is the resolution enhanced image which will be of the size 512 x 512. The zero padding preserves the images informations in an efficient manner and hence the distractions in the enhanced images were much reduced. The performance of the process is measured based on the PSNR and the MSE value calculated. The high PSNR value indicates that the images contains minimum distortions. The PSNR value is high and the MSE value is low in our proposed Modified wavelet based zero padding. The SSIM value is also measured and it is near to 1 in most of the cases which again proves that the proposed method is more efficient.