Single-image superresolution of self-similar textures
Ido Zachevsky, Yehoshua Y. Josh Zeevi · 2013
Single-image superresolution has become a widely-studied subject in image processing in recent years. Although considerable effort has been devoted in this context to contour enhancement, much less has been done to improve the textures of a degraded image. In this study, we present a novel algorithm which utilizes the power-law spectra of approximated 1/f processes, fitting a model of degraded natural textures to recover the information lost by blurring. A mosaic of realizations of the approximated 1/f processes is first imposed on the degraded texture, and a deblurring process is then applied. The entire process is iterated until convergence. This algorithm exploits the self-similarity, characteristic of textures of natural images, and recovers the missing high-resolution information without using prior information of any specific image.