近年來隨著深度卷積神經網路的發展，圖像超分辨率技術上有了顯著的進步。然而大多數研究者皆專注於讓模型順著單一的倍率進行訓練。只有少數人才專注於製作一個能通用於各式各樣倍率的模型。

我們參考了前人提出的單一模型任意解析度之研究，並做出改良。參考之模型為一自動編碼器結構。編碼器用於取得圖片內隱藏存在的特徵，而解碼器則用於將得來的特徵還原成各種使用者所指定之解析度之圖片。在本文當中，我們找來了一個能取得更加豐富特徵的編碼器，以及一個能更詳細還原圖片之解碼器。經過實驗證明，本文提出之模型 RRDN-NLIIF(殘差的殘差密集網路結合新局部隱性圖片表示函式之簡寫)在評估指標PSNR上有比原模型更為良好的結果。

With the development of deep convolutional neural networks, the technic of Image super resolution makes a remarkable progress. While most researchers focus on training one model with one kind of scale, only some focus on making a model that can be adapted to any scale.

We refer to prior work on a single model arbitrary resolution and improve on it. The model we refer to is an auto-encoder structure. The encoder is used to get the feature maps of the image we input, and the decoder is used to reconstruct these feature maps to any resolution the user is specific to. With the experiment results, we show that our model RRDN-NLIIF, which is short of Residual in Residual Dense Networks with New Local Implicit Image Function, makes a better performance than the one we refer to on PSNR metric.

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