目前數位相機（鏡頭）是攜帶式的設備（如智慧手機和平板電腦）的重要組成部分，而大部分人們所擁有的智慧手機，可隨時隨地拍照以記錄生活，但相機（鏡頭）拍攝的這些照片可能會受到雜訊的影響，導致後續圖像分析，例如在圖像中進行圖像識別、物體跟踪和物體分類，本篇論文在這基礎上開發了一種有效的去雜訊的組合架構。
本論文提出了可變重疊平均濾波（AOAF）和混合池聚焦式優化網絡（MARNs），首先，我們應用AOAF輸入具有雜訊的圖像以獲得初步過濾結果，其中雜訊像素經過AOAF去除雜訊後並嘗試恢復原來圖像的樣貌，接下來以AOAF所輸出的圖像作為MARNs的輸入，產生更細緻化的圖像，並在該圖像中更進一步地重建圖像細節和邊緣，實驗結果證明，我們的方法相對於最新技術具有良好的去雜訊能力。

Cameras are essential parts of portable devices, such as smartphones and tablets. Mostpeople have a smartphone and can take pictures anywhere and any time to record their lives. However, these pictures captured by cameras may suffer from noise contamination, causing issues for subsequent image analysis, such as image recognition, object tracking, and classification of an objectin the image. This paper develops an effective combinational denoising framework based on theproposed Adaptive and Overlapped Average Filtering （AOAF） and Mixed-pooling Attention Refinement Networks （MARNs）. First, we apply AOAF to the noisy input image to obtain a preliminarily denoised result, where noisy pixels are removed and recovered. Next, MARNs take the preliminary result as the input and output a refined image where details and edges are better reconstructed. The experimental results demonstrate that our method performs favorably against state-of-the-art denoising methods.

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