ABSTRACT

The subject of extracting particular high-resolution data from low-resolution images is one of the most important digital image processing applications in recent years, attracting much research. This paper shows how to improve the resolution of real images when given image is in the degraded form. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, and noisy and downsampled measured images. To obtain this result the use an iterative nonlinear restoration blind deconvolution maximum likely-hood algorithm imposing the low frequencies complete data of the original low-resolution image and the high-resolution data present only in a fraction of the image which suppresses the noise amplification and avoid the ringing in deblurred image. Our results show that a high resolution real image derived from superresolution methods enhance spatial resolution and provides substantially more image details.