臺灣大學: 電信工程學研究所丁建均張緯德Chang, Wei-DeWei-DeChang2013-03-272018-07-052013-03-272018-07-052012http://ntur.lib.ntu.edu.tw//handle/246246/252650隨著科技的進步，對於影像品質的要求也隨之變高，因此，模糊影像處理變的越來越重要。在本篇論文當中，模糊影像取得的來源為顯微鏡系統，與一般的模糊影像相同，顯微鏡影像仍然可以被視為目標影像與點擴散函數的作用。 在顯微鏡影像當中，由於鏡片是我們自己設計的，因此幾乎可以獲得一個正確的點擴散函數。根據已知的點擴散函數，影像還原程序變成了”非盲”的解摺積問題。然而，即使我們知道了模糊的參數，要完美的還原成目標影像依然沒有那麼容易。 在”非盲”的影像重建方法中，被分類為正規化方法的Richardson-Lucy 演算法是一個很受歡迎的影像重建方法，它利用迭代次數來換取還原影像邊緣的銳利度，然而，迭代次數與還原影像邊緣的銳利度間存在著一個權衡，越多的迭代次數會再邊緣附近造成越多我們不希望得到的光暈現象，因此，在本篇論文當中，我們提出了一個階層式的方法同時地降低了光暈現象並且讓還原的影像也能夠保持銳利的邊緣。此外，我們提出的方法也大幅地降低了計算複雜度。 另外，在影像重建的問題中，Wiener filter是另外一個受歡迎且被歸類為”非盲”的影像重建方法，但是，由於雜訊在我們模糊影像中的不確定性，Wiener filter的表現大幅受到了限制。在這篇論文當中，我們提出了另外一個影像重建的方法:邊緣成員度影像重建演算法。針對無法得知的雜訊問題，我們利用MMSE公式推導得出另一種形式的Wiener filter並且利用成員度的概念獲得了具有良好表現的還原影像。With the progress of the technology, the requirement of the image quality is getting higher and higher, therefore, the blurred image processing is getting more important. In this thesis, the blurred images from microscope system are used. Similar to ordinary blurred image, the blurred images from microscope system are also created by a point spread function (PSF). In the microscope system, a true PSF from designed lens can almost be obtained. According to the known PSF, the image restoration procedure become to a non-blind deconvolution problem. However, even if the blurring kernel is known, we cannot easily restore an image perfectly. In the non-blind image reconstruction, Richardson-Lucy (RL) algorithm, classified to regularization methods is a popular approach for image reconstruction, which uses more iteration times for the sharpness of edge regions in exchange. However, it is a tradeoff that if more iteration times we use, the more undesired ringing artifacts will be produced. In this thesis, to reduce the ringing artifacts, an image deblurring method of pyramid based RL algorithm is proposed. The proposed methods reduce the ringing artifacts and contain the sharpness of edges. Moreover, the computational complexity is also significantly reduced. Moreover, the Wiener filter is another popular approach in the non-blind image reconstruction. However, due to the uncertainty of noise, the performance of the Wiener filter is restricted. In this thesis, we proposed an edge-membership based image reconstruction algorithm, which focus on the noise uncertainty problem produced from derived Wiener filter. Furthermore, the membership concept is also applied. The experimental results show that our approach has good performance of restored image.140 bytestext/htmlen-US模糊影像重建blurred image reconstructionthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/252650/1/index.html