近年來，神經網路風格轉換因其令人印象深刻的成果而獲得許多關注。然而，它始終難以被良好調控，但在該領域中卻鮮少有人特別著墨於此問題。從先前研究論文的色彩控制方法所產生的神經網路風格轉換成果圖來看，卻還是有著色彩與筆觸不一致及顏色不能正確的被表現的問題。在本篇論文中，我們提出了一個兩步驟流程來解決色彩調控的問題。首先，我們將圖像顏色以加法混色模型所做的初步分類結果，依據 HSV 色彩空間中的色相通道，來進行切分及整併，使圖像顏色得以被分成多個具有代表性的顏色分類。再來，我們所提出的方法以用來解二分圖中最小權重和的匈牙利演算法，去找出顏色分類間的最佳配對組合。接著，我們分別對各個顏色分類配對組合進行色彩轉換。我們在實驗中展示了透過我們提出的方法，可以保留色彩與筆觸間的相依性且同時維持原色特性於神經網路風格轉換輸出圖像。最重要的是，我們也透過實驗來展現神經網路風格轉換之輸入風格圖中顏色面積比例對於神經網路風格轉換的重要性。

Recently, neural style transfer gains a lot of attentions by its impressive results. However, it still suffers from lack of controllability. With previous proposed color controlling methods, the result image from neural style transfer either has inconsistent color and texture rendering, or incorrect rendering colors. In this thesis we propose a two-step workflow to address the color controlling problem. In the first step, we classify image colors into several significant color groups with Hue channel in HSV color space as color entry by merging and splitting the initial color classification based on additive color mixing model. In the second step, our proposed method finds the best match between color classes by using the Hungarian algorithm to solve minimum-weights matching problem in bipartite graph. After that, we conduct color transfer class by class to each color pairs. In our experiments, we demonstrate that our proposed method can effectively generate results of neural style transfer that keep the consistency between color and texture while maintaining color characteristics at the same time.

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