Fractional-Grey Wolf optimizer-based kernel weighted regression model for multi-view face video super resolution

Due to the advancement of the intelligent surveillance system in recent days, security and protection cameras are installed even in small shops, but the qualities of the image captured by surveillance cameras are low. The technique used for reconstruction of the high-resolution images from observed low-resolution image is called as super-resolution techniques. In order to alleviate the resolution problem and to provide desired information, fractional-Grey Wolf optimizer-based kernel weighted regression model is developed in this paper for multi-view face video super-resolution. Here, a new optimal kernel weight matrix for the interpolation of the super-resolution image is generated using the proposed FGWO algorithm, which is newly developed by integrating the GWO with fractional calculus. The experimentation of the proposed system is carried over UCSD face video databases, and the performance results are analyzed using SDME, PSNR, and SSIM with various existing techniques. The experimental results demonstrated that the proposed method improved the performance of super-resolution by achieving the maximum PSNR, SSIM and SDME value of 49.5909, 0.99 and 87.51 dB.