隨著科技的快速發展，人們對影像及影片解析度的要求逐漸提高，但是許多情況下使用者僅能取得低解析的影像，為了讓這些低解析的影像能夠被使用，此時便需要影像超解析的輔助，影像超解析透過將低解析影像進行處理，輸出高解析影像給使用者。
常見的影像超解析可以分為四大類，分別為內插式、學習式及範例式、重建式、邊緣式及梯度式，本文的方法是透過運用重建式及梯度式影像超解析的技術，結合影像興趣區及影像超解析，實現一種新的影像超解析方法。此演算法共分為三個步驟，分別處理以下三層的興趣區，首先是第一層興趣區，將經過基礎縮放的低解析影像進行全區域高斯高通濾波處理。接著在第二層興趣區，透過設定閥值以篩選梯度影像區域，找到影像中主要物體區域，對物體所在的區域進行二次高斯高通濾波處理。最後一層興趣區，將影像平滑區域篩選出來，使用維納濾波器進行雜訊處理，最終輸出即為處理過後的高解析影像。
在實驗結果章節，為了驗證本文所提出的演算法的可行性，總共使用263張影像進行數據分析，同時對兩倍放大及四倍放大進行實驗，而實驗結果證明本文所提出的演算法能夠在超解析處理的時間耗費及輸出影像的峰值信噪比兩者間取得平衡。根據本文第五章之實驗結果，本文演算法處理速度快速且輸出結果良好，本文演算法的處理速度大幅優於學習式及範例式影像超解析，且峰值信噪比及結構相似性與兩者相當接近。此外本演算法無須任何訓練、學習，僅需一張輸入影像即可完成處理，本文所提出的演算法的處理過程中，無須大量的影像資料庫進行訓練、學習，僅透過輸入一張低解析影像即可以進行影像超解析處理。

This thesis proposes an image super-resolution (SR) algorithm, based on multi-layer region of interest (ROI).
Image super-resolution provides a solution to the problem of limited resolution in image or video. However, the algorithms which have high quality of output images need to spend much time in training. The algorithms which have lower time complexity have low quality of output images.
The core idea of the proposed approach in this thesis are: (1) Proposing an image super-resolution algorithm which is getting a balance between time complexity and output image quality; and (2) Combining the concept of region of interest with image super-resolution to improve the output image quality. The algorithm enhances different image details in different layer of region of interest.
The simulation results in this paper has shown the algorithm gets a well balance between the quality of output image and time complexity.

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