隨著科技發展，各式各樣消費性電子產品誕生，如：數位電視、電腦顯示器、智慧型手機、平板電腦等等，每種設備有不同的解析度，為在不同的設備上顯示圖片或影像，須改變解析度尺寸。此種改變解析度尺寸的方式稱作插值法，插值法是一種將低解析度影像放大為高解析度影像的技術。插值法通常假設影像成像時僅遭受微小雜訊干擾，但實際上常常此假設無法成立，例如：戶外夜晚拍攝、室內光源不足時拍攝等等，影像被嚴重雜訊干擾。此時若直接將影像經插值法放大，影像中的雜訊也隨之放大，使影像品質不佳。直覺上，通常會將影像先降噪爾後再插值放大，然而，經降噪處理後，有些影像結構特性改變，令之後的插值法失效，產生許多人造瑕疵。於是我們提出一種基於非區域加權聯合影像降噪與插值，先通過非區域加權方式降噪，做法為轉換到頻域空間作相似區塊搜尋，再作非區域加權方式降噪，由此可得更佳的降噪結果。非直接將降噪後的影像放大，而是進一步將降噪過程中使用的三維相似區塊的資訊傳遞結合到影像插值法中，插值法採用加權最小平方法，將此三維相似區塊合併到此插值法，在低解析度網格窗口估計出更準確的區域參數估計，利用此區域參數估得失去的高解析像素，避免影像結構特性因降噪處理產生的改變，進而還原感官較佳且較少人造瑕疵的高解析度影像。

Almost image interpolation method in existence assume that image is contaminated with low noise level. In practice, on the low light situation such as outdoor night scene, indoor scene, image is corrupted with high noise level in imaging process. On this situation, first image is denoised, then is interpolated. After denoising process, the structure and texture property of image will change so that plenty of assumptions of interpolation method is not robust, so it bring about artifacts such as jaggies, blurring, and ringing, on the interpolated image. For this reason, we propose joint image denoising and interpolation under the nonlocal weighting estimation framework. For noisy image, assemble similar blocks and stack 3D blocks via nonlocal block matching on the denoising process, and then propagate the 3D blocks for more accurate missing HR pixel estimation on interpolation process. Compared with the progressively separate process of denoising and interpolation. Because of more correct local structure estimation, the interpolated image preserve edge structure well and cause less defects.

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