Since over three decades, computers have been widely used for processing and displaying images. The ability to process visual information from a super resolution image can enhance the information present in the image. The motivation is from a human eye which takes in raw images (noisy, blurred and translated) and constructs a super resolution image. An image with improved resolution is desired in almost all of the applications to enhance qualitative features and is reported to be achieved by Super Resolution Image Reconstruction (SRIR). Some low resolution images of same scene which are usually rotated, translated and blurred are taken to form a super resolution image. The image registration operation orients translated, scaled and rotated images in similar way to that of source image. Lifting Wavelet Transform (LWT) with Daubechies4 coefficients is applied to color components of each image due to its less memory allocation compared to other techniques. Further Set Portioning in Hierarchical Trees (SPIHT) algorithm is applied for image compression as it possess lossless compression, fast encoding/decoding, adaptive nature. The three low resolution images are fused by spatial image fusion method. The noise component is removed by dual tree Discrete Wavelet Transform (DWT) and blur is removed by blind deconvolution or iterative blind deconvolution. Finally, the samples are interpolated to twice the number of original samples to obtain a super resolution image. The structural similarity for each intermediate image compared to source image is estimated via objective analysis and high structural similarity is observed for image constructed by the proposed method.