کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6951744 | 1451702 | 2018 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Blind deblurring with sparse representation via external patch priors
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
In this paper, a blind image deblurring method is proposed using sparse representation with external patch priors. Different from traditional sparse-based methods that employ only internal priors from blurred images, additional external information is adopted to reconstruct latent images. In details, the Expected Patch Log Likelihood (EPLL) is introduced as a useful tool to describe external patch priors with a pre-trained Gaussian mixture model. With a set of operations, the EPLL is subsequently incorporated as a regularization term into the existing sparse-based deblurring model. Meanwhile, the dictionary is also carefully designed for each patch of the latent image, where atoms are obtained from the covariance matrix of the corresponding Gaussian component. A deblurring framework is further presented along with our sparse-based model. The solutions are respectively given to efficiently optimize the latent image and the blur kernel with an iterative procedure. The experiments demonstrate that our proposed algorithm achieves a competitive performance compared with the state-of-the-arts. Especially, it not only can obtain more accurate kernels for the deblurring, but also outperforms in noise reduction and artifact suppression for the restored images.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 78, July 2018, Pages 322-331
Journal: Digital Signal Processing - Volume 78, July 2018, Pages 322-331
نویسندگان
Yibin Tang, Yimei Xue, Ying Chen, Lin Zhou,