對於有歷史且老舊的竣工圖進行二值化是一項新的具有挑戰性的工作，尤其是在消除三個因環境或時間所造成的瑕疵（即雜點，泛黃區域和摺痕）的同時，還要很好地保留住清楚的前景。在本文中，我們提出了一種新的深度學習二值化方法，以生成高品質的二值化竣工圖。基於該方法所測試出來的二值化圖，我們的實驗數據可看出，就精確度、PSNR（峰值信號噪聲比）和其視覺效果而言，我們的方法優於現有的九種二值化方法，其中也包含了兩種基於深度學習所提出的二值化演算法，且我們所提出的深度學習模型也是三種深度學習模型裡最快速且參數量最少的。

Binarizing historically degraded asbuilt drawing maps(HDAD) is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a convolutional neural networkbased (CNNbased) binarization method to produce highquality binarized HDAD maps. Based on the testing HDAD maps, the thorough experimental data demonstrated that in terms of the accuracy, PSNR (peaksignal tonoiseratio), and the perceptual effect of the binarized HDAD maps, our method substantially outperforms the nine existing binarization methods. In addition, with similar accuracy, the experimental results demonstrated the significant executiontime reduction merit of our method relative to the retrained version of the stateoftheart CNNbased binarization methods.

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