範例式高解析方法的特徵組合比較

A Comparison of Feature-Combination for Example-Based Super Resolution

曹仁輝(Jen-Hui Tsao)

指導教授 ： 顏淑惠

淡江大學/工學院/資訊工程學系碩士在職專班/碩士(2013年)

https://doi.org/10.6846/TKU.2013.01058

摘要

高解析在電腦視覺上一直以來都是一個很重要的題目，本文中我們經由實作一個完整的範例式高解析演算法來對影像高解析領域中常用的特徵進行的比較。實驗中我們開發了一個疊層式框架來解決在特徵組合中決定各個特徵權重的問題，並且改善了效能問題。在實驗結果裡我們可以看到各個獨立以及組合式的特徵對驗實產生的影響。在實驗的最後，我們對框架做了修改使其具有可適性，修改過後的框架不但可以降低運算量（高運算量是範例式高解析演算法的常見問題）而且對於不同的影像資料庫有著更佳的可適性。

關鍵字

高解析 ； 基於範例 ； 雙立方內插法 ； 疊層式

並列摘要

Super resolution (SR) in computer vision is an important task. In this paper, we compared several common used features in image super resolution of example-based algorithms. To combine features, we develop a cascade framework to solve the problems of deciding weights among features and improving computation efficiency. In the experimental results we can see the effectiveness of each independent or combined features. Finally, we modify the framework to have an adaptive threshold such that not only the computation load is much reduced but the modified framework is suitable to any query image as well as various image databases.

並列關鍵字

super resolution(SR) ； example-based ； bicubic ； cascade

參考文獻

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