Robust Multiframe Super Resolution Reconstruction of Different Gray Scale Images for Real Time Data with and without Adaptive Filter

High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Multi-frame super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. Super resolution reconstruction consists of registration, restoration and interpolation phases, once the low resolution image are registered with respect to a reference frame then restoration is performed to remove the blur and noise from the images, finally the images are interpolated using bilinear interpolation. Image super-resolution creates an enhanced high resolution image using multiple low resolution images of the same scene. A typical image formation model introduces major three parameters i.e. blurring, aliasing, and added noise. Super resolution is designed to jointly reduce or remove all these three parameters. While the first superresolution algorithm appeared over 20 years ago, only recently people explored the performance of these algorithms. However, these papers have explored only objective MSE performance. In this paper, we use subjective testing to explore the visual quality of images enhanced with super-resolution. MATLAB based results are shown in this paper with and without adaptive filters with better PSNR values and percentage change in pixel values. The proposed approach has succeeded in obtaining a High-resolution image with a better PSNR value and good visual quality. KeywordsSuper-resolution; Reconstruction; Bilinear interpolation; Blurring; Aliasing.