近年來，由於科技的進步，使得手機與相機越來越發達，也使得人們拍照的越來越容易，相片數量因而大量增加。大部分的人拍照只為記錄生活，但如何拍出美感就值得探討。專業攝影師利用他們藝術天分拍下的那一瞬間表達的情感，而且部分的攝影師會用他們專業的美學技術來去增強照片進而更加呈現當時的意境。如何讓電腦學依據模糊的美學定義來分析美學相片與美學修圖來幫助人們是一個有意義且困難的挑戰。
本篇論文提出了一個基於當代美學學習之影像增強系統，在增強相片之前，我們使用許多方法去分析相片中的元素，例如高斯拉普拉斯變換、RMS方法、顯著圖方法等等，我們共有16種特徵去偵測相片中美學程度。增強部分我們提出一個新的概念使用分群結合分類來去分析我們提取16種特徵，這些方法可以依據我們美學數據集產生出4種風格群，當一張相片被決策樹方法判斷是不好的照片時，可以用決策樹的特性知道哪種特徵需要加以修改。我們列出10種可以修改照片的強大方法，例如伽馬校正方法、高斯模糊方法等等。最後我們使用區間減半法讓所增強的數值加速近似決策樹建議的增強值。
實驗部分為判斷美學相片，經實驗結果顯示平均正確率為96.8%，增強相片部分每一群代表一種相片的風格，我們使用決策樹特性來加以步步增強或降低不同的特徵，其相片後製成果就跟專業攝影師修圖一樣完美。

In recent years, the rise of smartphones and digital cameras makes it easier to take photos and a mass amount of photos are spread on the Internet. Photographic aesthetics is some sort of art which is expressed by the professional photographers’ aesthetic sensibilities and emotion. Moreover, many professional photographers make adjustments to the photos in post, and let photos much become more beautiful and meet the conditions of photographic aesthetics rules. Enhancing the images followed by ambiguous photographic aesthetics become a big task for computer.
In this thesis, an automatically image enhancement based on the aesthetics images dataset from the internet is proposed. We used many method to analyze an image such as RMS method, Laplace of Gaussian method, saliency map method, Gabor filter method and so on. We can use above sixteen features extracted from image to judge an image is good or not. We present a new concept to enhance images by using cluster styles which are generated from X-means and CART decision tree. When an input image is judged as a bad image by CART decision tree, the reason can be traced back by the decision tree characteristic to know which features needs enhancement. We list ten features which can enhance image efficiently such as gamma correction, Gaussian blur and so on. We use Interval Halving method to approach the value which come from giving suggestion of a feature by CART decision tree based on contemporary aesthetics criteria.
In the experiments, we apply cluster and classification to our dataset, and the average of cluster’s accuracy is 96.8%. In the enhancement part, we use CART decision tree aesthetic suggestion which means some feature are not enough or some feature are too high that can enhance our image step by step. Then we can get differently image style result like professional photographers do.

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