近年來影像超解析技術的應用顯著地增加，其主要目的是從一張低解析度的影像生成一張高解析度的影像。在本論文中，我們提出一個有效率的超解析演算法。其中我們設計了16個濾波器（filter）來適當地取得對應的方向性細節，接下來我們從低解析度影像中經由所設計的濾波器所取得的方向性細節用來建立多重線性迴歸模型（multiple linear regression model）和脊迴歸模型（ridge regression model），並且利用這些模型以及從初步高解析度影像中相對應的方向性細節來分別預測高解析度影像中區域的細節和全域的細節。為了能夠自適性地利用影像中直線線段的特徵，我們利用坎尼邊界偵測和霍式轉換來建立區域性重建模型。由實驗結果可以清楚的看到，我們所提出的演算法同時提供較好的影像品質和好的客觀評比指標。

The application of image super-resolution technologies in recent years has increased noticeably. The main purpose of super-resolution is to generate high-resolution (HR) images from low-resolution (LR) images. In this Thesis, an efficient SR algorithm is proposed. Multiple linear regression models and ridge regression models are established with sixteen oriented details from LR images by the designed filters. Afterward, the two reconstruction models are utilized respectively to estimate global and local details of HR images with the corresponding oriented details that are acquired from corresponding preliminary HR images by the same filters. For more adaptively utilizing the straight line segments characteristics in an image, Canny line detection and Hough transform are applied to build local reconstruction model. Experimental results show that the proposed algorithm produces HR images with better in both the visual quality and the objective measurements.

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