The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a highresolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based...
In this paper, we present a novel single-image superresolution (SR) algorithm that utilizes clustering-based global regression to generate desired high-resolution (HR) patch with its low-resolution (LR) counterpart. Propagation filtering can achieve smoothing over image while preserving image context like edges or textural regions. Furthermore, to preserve the edge structures of super-resolved image...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.