超解析度演算法使用於線上學習

Abstract

由於影像顯示裝置的進步，人們對影像的品質要求越來越高。但是高級的影像擷取設備卻依然較為昂貴，所以我們常轉而採用軟體方式來加強影像品質。超解析度演算法即為當前十分熱門的影像處理研究領域之一，應用的範圍很廣，包括軍事、監視系統等等。 近年來，線上數位學習的需求越來越高，但是大部分的教師並沒有較好的影像擷取設備，故無法錄製高品質的教學內容。本篇論文即是針對一般教學的影像加以強化，特別是以黑板、白板或投影幕為教具的教學方式。 超解析度演算法雖然可以增強影像解析度，但是大量的運算時間和未參考影像本身的內容是大部分超解析度演算法所擁有的問題。在本篇論文中，使用了邊緣偵測來估計小區塊之中影像邊緣的稠密程度，並使用平均移動法來進行色彩分割。綜合上述兩種資訊來判斷何處是教學影像中，人眼最關注、次關注及未關注的區域，並在不同的區域使用不同複雜度的演算法。 實驗結果顯示，我們僅需使用高複雜度演算法來處理全圖約20%的區塊，大幅降低運算時間，同時也可以得到品質良好的影像輸出。

Because of the development of image display devices, people require much better quality of images. The high-level image capture devices are still expensive, therefore we usually use software to enhance the quality of images. Super- resolution algorithm is a popular research domain in digital image processing, and the applications are widespread, including the military, surveillance, and so on. Recently, the requirement of on-line digital learning is much higher, but many teachers do not have good image capture devices, they cannot record teaching contents with high quality. In this paper, we propose a method to enhance the normal teaching images, especially for the teaching mode using black board, white board, or projection screen. Though super-resolution algorithm can enhance the image resolution, the large execution time and disregarding the image content are the problems in majority of super-resolution algorithm. In this paper, we use edge detection to estimate the image edge density of small blocks, and use mean shift to implement color segmentation. We can integrate the above information to determine where people pay attention to mostly, where secondly, and where we do not care, and use different complexity algorithms to process them. By the experiment result, we only have to process 20% area of the whole image, and decrease execution time significantly. Simultaneously, we can only get an image output with good quality.