The recent rapid development in image capturing devices provides many opportunities to people to get a better quality camera available in a market. However, due to their high prices many people didnt go for it and they still want to use their existing digital cameras. On the other side demand for High Resolvtion (HR) images is tne cry of the day same as more and more bandwidth are required day by day for different bandwidth-hungry applications in communication area. Limited image resolution due to various degradations factors such as noise andblur leads to a number of problems in different multimedia applications such as object recognition in video surveillance. This fact brings out the demand for study different image processing tasks such as image enhancement and image restoration that aim to improve efficiently the interpretability of visualinformation lies in images for us. However, they didnt provide desired results afier certain level of improvement. Super Resolution (SR) technology is a real science andengineering rather than fiction utilized to combat limited image resolution problem andpromise to produce a desired HR output from a sequence oflow resolution (LR) images,which forms the core issue discussed in this dissertation. Our work mainly iocuses onconstruction of a Multi Input Single Output (MISO) system for SR. The MISO systemhas an input of multiple images, which can be taken by a video camera or still imagecamera and the output is a single image with higher resolution than the input images. Theaim of generating a HR image is to uncover the details of the scene and increasing thenumber of pixels as well. The basis for MISO super resolution reconstruction is providedby multi-frame analysis while the quality improvement is caused by fractional-pixeldisplacements existeci between multiple input images. Due to all this, SR allows toovercome the inherent limitations of the imaging system without the need for additional.hardware modification. Thats the reason that today it has a booming market demand.　　 This thesis addresses different aspects of MISO image SR. We explore various schemesin different processing domains to produce better HR image and to improve theperformance of existing techniques as well.　　 In the first contribution, we perform well-known grid analysis for comparing theperformance of various existing MISO image SR techniques. We considered differentexisting techniques for this purpose and taken different valuable factors into account tochoose a better technique among them.　　 In the second contribution, we formulate an efficient algorithm based on recentlyintroduced curvelets and used the absolute values of kurtosis as an input. Simulationresults show that proposed algorithm provides significant PSNR gain and outperfonnexisting transform-based technique.　　 The third contribution of the thesis addresses use of statistics to develop. a simple pre-processing strategy for image SR reconstruction that exploit the optimal selection of LRinputs and generate a HR image of same quality as produced by utilizing all the inplit LR images. Simulation results validate our proposed strategy using different kind of images.　　 The fourth contribution deals with implementation of an integrated SR reconstruction algorithm based on interpolation of cropped LR frames extracted from a low quality video surveillance sequence to perform effective license plate recognition. The proposed algorithm provides an altemative to modem costly surveillance cameras.　　 Finally, the novelty of last contribution is investigation of a relationship between LR input images and the resultant HR image for existing MISO SR techniques. We also address one of the most important quality assessment issues tor MISO reconstruction- based image SR techniques in this contribution as well.