Abstract Increasing model resolution can improve the performance of a data assimilation system because it reduces error, more optimally use high-resolution observations, and with an ensemble method forecast error covariances are improved. However, increasing scales cubical increase computational costs. A that effectively is introduced here. The novel approach called “Super-resolution assimilati...

Super-resolution (SR) is a technique that produces a high resolution (HR) image via employing a number of low resolution (LR) images from the same scene. One of the degradations that attenuates performance of the SR is the blurriness of the input LR images. In many previous works in the SR, the blurriness of the LR images is assumed to be due to the integral effect of the image sensor of the im...

The field of super-resolution has a wide area of applications. In order to display relatively low-quality content on high-resolution displays, the need for super resolution algorithms has become an urgent market priority. A method of super-resolution based on projectonto-convex-sets (POCS) is proposed in this thesis. In the super-resolution process, a set of low-quality images is given, and a s...