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Learning to Deblur Images with Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2018-05-01 , DOI: 10.1109/tpami.2018.2832125
Jinshan Pan , Wenqi Ren , Zhe Hu , Ming-Hsuan Yang

Human faces are one interesting object class with numerous applications. While significant progress has been made in the generic deblurring problem, existing methods are less effective for blurry face images. The success of the state-of-the-art image deblurring algorithms stems mainly from implicit or explicit restoration of salient edges for kernel estimation. However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation. In this paper, we address the problem of deblurring face images by exploiting facial structures. We propose a deblurring algorithm based on an exemplar dataset without using coarse-to-fine strategies or heuristic edge selections. In addition, we develop a convolutional neural network to restore sharp edges from blurry images for deblurring. Extensive experiments against the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. In addition, we show that the proposed algorithms can be applied to image deblurring for other object classes.

中文翻译:

学习使用示例对图像进行模糊处理

人脸是具有许多应用程序的一种有趣的对象类。尽管在通用去模糊问题上已经取得了重大进展,但是现有方法对于模糊人脸图像的效果较差。最新的图像去模糊算法的成功主要源于显着边缘的隐式或显式恢复,以进行核估计。但是,现有方法效果不佳,因为只能从模糊的人脸图像中还原出很少的边缘用于内核估计。在本文中,我们通过开发人脸结构来解决人脸图像去模糊的问题。我们提出了一种基于示例数据集的去模糊算法,而无需使用从粗到精的策略或启发式边缘选择。此外,我们开发了卷积神经网络以从模糊图像中恢复清晰边缘以进行去模糊处理。针对最新技术的大量实验证明了所提出算法对人脸图像去模糊的有效性。此外,我们证明了所提出的算法可以应用于其他对象类别的图像去模糊。
更新日期:2019-05-22
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