V - Channel magnification enabled by hybrid optimization algorithm: Enhancement of video super resolution

Abstract

Although being a really active area of research, television super-resolution remains a difficult problem. Additionally, it is noted that the blur motion and computational crisis hinder the enhancement. As a result, the goal of this research is to present a brand-new smart SR framework for the camera shot. To create High Resolution (HR) videos, first frames in RGB format are converted to HSV and then the V-channel is enhanced. In order to create enriched video frames, a high - dimension grid with enhanced pixel intensity is then created. This paper introduces a particular progression to enable this: Motion estimation, Cubic Spline Interpolation, and Deblurring or Sharpening are the three methods. By carefully adjusting the parameters, the cubic spline interpolation is improved during operation. A brand-new hybrid technique dubbed Lion with Particle Swarm Velocity Update (LPSO-VU), which combines the principles of the Lion Algorithm (LA) and Particle Swarm Optimization (PSO) algorithms, is presented for this optimal tuning purpose. Finally, using the BRISQUE, SDME, and ESSIM metrics, the adequacy of the method is contrasted to other traditional models, and its superiority is demonstrated. Accordingly, the analysis shows that the suggested LPSO-VU model for video frame 1 is 16.6%, 25.56%, 26.2%, 26.2%, and 27.2% superior to the previous systems like PSO, GWO, WOA, ROA, MF-ROA, and LA, respectively, in terms of BRISQUE.