- 學位論文
以SURF的方法為基礎使用多視角影像實現 超解析度
SURF-based Super Resolution using Multi-View Images
摘要
超解析影像是指從一組經過次像素位移的低解析度影像重建出高解析 度影像。每一個低解析度影像均包含有關景物的新資訊，而超解析的目的 就是要結合這些低解析度影像以得到更高解析度的影像。一般來說可分為 兩個主要工作：(1)影像對準是將拍攝同景物之兩個或更多的影像重疊的程 序；(2)影像重建是將低解析度影像重建成高解析度影像。本論文在影像對 準部份中使用SURF (Speeded Up Robust Features) 以改善高解析影像的品 質。接著本方法與Fourier Upsampling， SIFT (Scale-Invariant Feature Transform)， 和Keren 的方法比較。實驗結果顯示，使用SURF 可以比其 他方法執行的更好且更快。
並列摘要
Image super-resolution refers to the reconstruction of a high resolution (HR) image from a set of low resolution (LR) images which are sub-pixel shifted from each other. Each low resolution image contains new information about the scene and super-resolution aims at combining these to give a higher resolution image. In general, it can be broken into two major tasks: 1) image registration which is the process of overlaying two or more images of the same scene and 2) image reconstruction of the LR images into an HR image. This thesis uses the SURF (Speeded Up Robust Features) for image registration to improve the quality of the HR image. This method then compared with other methods, named Fourier Upsampling, SIFT (Scale-Invariant Feature Transform), and Keren. The experimental result shows that the SURF method can perform better and faster than the other methods.