隨著科技的進步，人們對於影像的視覺品質需求也越來越高，因此，影像解析度的提升也成為一個熱門的研究議題，其主要目的是藉由一張或是多張的低解析度影像來產生其高解析度影像，而我們希望產生出來的高解析度就像自然而非人工的影像，此一技術又稱為超解析，目前廣泛的被應用在高品質數位電視、智慧型手機、衛星影像、監視系統攝影機等設備上。而我們一般常用的的內插方法常常會出現模糊、鋸齒等不自然的現像。本論文所提出的方法，是基於一個簡單的內插法放大，並利用迴規模型來重建高解析度影像之演算法，並且加入一個去除鋸齒狀機制，實驗結果顯示我們的方法可以有效去除邊緣鋸齒狀並且能夠保留其邊緣資訊，使影像看起來自然且清晰，並且有良好的峰値信噪比。

With the advance of science and technology, the need for the visual quality of images is getting higher. Therefore, technology of super-resolution becomes a hot research topic. The main purpose of up-scaling is to obtain high-resolution images from low-resolution images, and the goal is to make the result of the upscaled image looks like natural image. This technique is also known as super-resolution. It had been widely used in high definition televisions, smart phones, satellite images and surveillance cameras. In general, the popular convolution-besed methods usually induce blurring artifacts and jaggy artifacts along slant edges. Therefore, in order to solved these problems, the proposed algorithm first removes the jaggy artifacts, and then adopts the regression model established from an LR image to reconstruct an HR image. Experimental results show that the proposed algorithm produces HR images with better visual quality.

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