標題: | Image super-resolution by estimating the enhancement weight of self example and external missing patches |
作者: | Lin, Fang-Ju Chuang, Jen-Hui 資訊工程學系 Department of Computer Science |
關鍵字: | Image super-resolution;Enhancement weight;Patch clustering;External superresolution |
公開日期: | 1-八月-2018 |
摘要: | Image super-resolution (SR) is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed, but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas. |
URI: | http://dx.doi.org/10.1007/s11042-017-5350-1 http://hdl.handle.net/11536/147950 |
ISSN: | 1380-7501 |
DOI: | 10.1007/s11042-017-5350-1 |
期刊: | MULTIMEDIA TOOLS AND APPLICATIONS |
Volume: | 77 |
起始頁: | 19071 |
結束頁: | 19087 |
顯示於類別: | 期刊論文 |