Abstract :
Image super-resolution are techniques aiming restoration of a high-resolution image from one or several low-resolution observation images, which offer the advantages overcoming some of the inherent resolution limitations of low-cost imaging sensors (e.g., satellite image, cell phone, camera’s or surveillance camera’s), and allow better utilization of the growing capability and noise free image of HR displays. Conventional image super-resolution approaches normally require multiple LR inputs of the same scene with sub-pixel motions. This paper attempts to undertake the study of the super-resolution restoration problem and improved resolution image is restored from several geometrically warped, blurred, noisy images. The super-resolution restoration problem is modeled and analyzed from the filters such as Median Filter, Adaptive Wiener Filter, Gaussian Filter these different noise densities have been removed between 10% to 65%. The Principal Component analysis (PCA) is the technique which is useful for improving the image sharpness after the process of de-blurring.
