English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49432/84393 (59%)
Visitors : 7453357      Online Users : 91
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105689

    Title: 利用細化核心於盲動態去模糊
    Other Titles: Blind motion deblurring using thinning kernel
    Authors: 陳品安;Chen, Pin-An
    Contributors: 淡江大學資訊工程學系碩士班
    顏淑惠;Yen, Shwu-Huey
    Keywords: 盲動態去模糊;細化核心;影像反卷積;Motion Deblurring;Thinning Kernel;Image Deconvolution
    Date: 2015
    Issue Date: 2016-01-22 15:02:44 (UTC+8)
    Abstract: 盲目影像清晰化主要分為兩個部分:估計過渡影像和估計blur kernel。由於輸入的模糊影像給予足夠的影像資訊,而blur kernel幾乎完全沒有資訊,因此過去的盲目影像清晰化的方法多著重於估計過渡影像的部分。本論文主要研究由照相機晃動導致的模糊影像,並著重於blur kernel的估計。首先,經由相機的晃動軌跡,對blur kernel模型提出簡單的推論與假設;接著以兩個步驟的方式來估計blur kernel,藉此改善資訊不足的問題。本論文先以較為寬鬆的條件求得blur kernel可能的外型,再以此外型為基準,使用細化演算法求得blur kernel的骨架,以此骨架為中心,使用高斯分布的方式予以新的權重值,進行第二步的blur kernel估計,經過少數的迭代後即可得到更為準確的blur kernel。利用這個blur kernel,我們可以獲得更高品質的清晰影像。
    The task of blind deblurring usually consists of estimation of interim images and estimation of blur kernels. Due to little information in kernels comparing to that in interim images, when only a blurred image available, most of deblurring methods emphasis the estimation of interim images. However, the resulting kernel often is wider than it should be and degrades the quality of deconvolved image. To remedy the problem of wide kernel, we present a thinning scheme to better estimate a kernel. In this way, a clear image can be recovered from a camera-shake blurred one.
    To mitigate insufficient information of blur kernels, we make simple inferences and assumptions for kernels based on the trajectory of the camera shake. Under those inferences and assumptions, we use a three-step approach to estimate the blur kernel. Firstly, we relax the condition to find the shape of blur kernel. Next, we use a thinning algorithm to obtain the skeleton of blur kernel. Thirdly, we reweight the blur kernel by Gaussian distribution. By repeating these steps a few times we can get a more accurate blur kernel. Finally, we can reconstruct a high quality deblurred image by using the blur kernel. The proposed method is tested by a public database of 32 blurred images produced by 8 different blur kernels. Our result outperformed two similar methods.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback