The major goal of a super-resolution image reconstruction method is to construct a single in-depth high-resolution image from a group of numerous low-resolution images of the scene taken from diverse positions. Since every low-resolution image retains a different view of the scene, it is likely to reconstruct an in-depth high-resolution image. Thus Image super-resolution is a key to overcome the material precincts of hardware competence. High-definition-television (HDTV) flat panel displays are present generation households. Nevertheless, an enormous amount of video is still in traditional definition format or has even a lower resolution. They also have relentless coding artifacts. Hence there is a need for techniques which can improve the video quality and that show all the traditional and low-resolution videos on panels with high resolution grids. The Iterative super-resolution reconstruction algorithms can accomplish this exigent chore by using the internal image models and an incorporated feed-back loop to control the output quality thereby improving resolution and lessening artifacts. This paper portrays the prospects of iterative reconstruction algorithms and also establishes a new super-resolution algorithm that is computationally very strong and efficient against motion estimation errors. Moreover, integration of additional control mechanism into this new algorithm is proposed. This algorithm enhances the flexibility for processing input video with diverse quality levels, detail levels and controls the sharpness in the output sequence. This method is robust in diminishing side-effects caused by incorrect motion estimation. © 2012 Euro Journals Publishing, Inc.