影像的清晰化是個很重要的問題，不論是對於影像的保存或是影像後續的處理都需要清晰的影像。針對於影像強化的技術有許多種的方式，其中之ㄧ的技術Unsharp Masking (UM)具有不錯的影像強化效果，本篇提出基於UM技術的影像強化方法，一個針對影像中雜訊的判斷技術，利用Lalpacinan的特性與Connected Component Analysis的技術找出雜訊的可能點，並且利用平均值取代掉該點雜訊，可以降低雜訊出現的程度，接著針對去雜訊後的影像使用Canny edge detector偵測出影像中物件的邊緣，將這些邊緣分類成輪廓與摺邊，並且給予不同程度的強化權重，利用UM的方使來強化影像，在我們的實驗結果中，對於平滑區域的雜訊有不錯的抑制效果，同時對於物件邊緣的部份加以強化，其結果與其他的方式比較起來over/under shooting 的現象降低許多，在數據上的表現也來的不錯。 Unsharp masking (UM) is an effective and popular method on image enhancement. However, it is sensitive to noise and tends to have over/under shooting problems. In this paper, we propose an improved UM-based technology for image enhancement. First, noises are detected and smoothed. Then, integrating the silhouette and crease edges (major and minor edges), we design an adaptive weighting method to enhance the contrast for edges. In this way, the major edges (silhouette) are sharpened more comparing to minor edges (crease). Hence, not only the over/under shooting problems are solved but the contrast on edges are properly enhanced. The proposed method has been compared to existing UM-based methods and the results are satisfying.
