Super-resolution Techniques based on Temporal Recursion using Optimal and Real Parameters

Abstract

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 possible to reconstruct an in-depth high-resolution image. Thus image super-resolution is a key to overcoming the material precincts of hardware competence. An enormous amount of video is still in traditional formats or at an even lower resolution. Some also has relentless coding artifacts. Hence, there is a need for techniques that can improve video quality and that show all the traditional and low-resolution videos on panels with high-resolution grids. Iterative super-resolution reconstruction algorithms can accomplish this exigent chore by using internal image models and an incorporated feedback loop to control output quality, thereby improving resolution and lessening artifacts. This article portrays the prospects of iterative reconstruction algorithms and establishes a new super-resolution algorithm that is computationally very strong and efficient against motion estimation errors.