淡江大學機構典藏:Item 987654321/120344
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120344


    Title: Deblurring a Camera- Shake Image Using a Thinning Kernel
    Authors: Yen, Shwu-Huey;Chen, Pin-An;Lin, Hwei-Jen
    Keywords: blind deconvolution;blur kernel;camera-shake;deblur;interim image
    Date: 2018-09-11
    Issue Date: 2021-03-20 12:10:18 (UTC+8)
    Abstract: The task of blind deblurring usually consists of
    estimation of interim images and blur kernels. Due to the lack of information in kernels compared to that in interim images, when only a blurred image is available, most of deblurring methods emphasis the estimation of interim images. However, the resulting kernel is often wider than it should be, thus degrading the quality of the deconvolved image. To remedy the problem of wide kernels, we present a thinning scheme to better estimate a kernel. In this way, a
    clear image can be recovered from a camera-shake blurred image. To mitigate the insufficient information of blur kernels, we make simple inferences and assumptions for kernels based on the trajectory of the camera shake. Under these inferences and assumptions, we use a three-step approach to estimate the blur kernel. Firstly, we relax the condition to find the shape of the blur kernel. Next, we use a thinning algorithm to obtain the skeleton of the 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 and our results outperform those of two similar methods.
    Relation: Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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