کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002851 | 1449921 | 2018 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Nonparametric blind SAR image super resolution based on combination of the compressive sensing and sparse priors
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
This paper by proposing a novel approach, is one the first works that addresses the highly ill-posed problem of nonparametric blind single image super resolution (SISR) of the synthetic aperture radar (SAR) images. Combination of an adaptive compressive sensing (CS) technique and some effective sparse priors, as a powerful regularizer in the both high resolution (HR) image reconstruction and the point spread function (PSF) estimation domains is the fundamental idea of the proposed method. This task is formulated as a new cost function to be minimized with respect to an intermediate reconstructed HR image patch and a nonparametric PSF kernel, according to the alternative minimization (AM) algorithm. To solve the optimization of cost function, a numerical scheme based on the conjugate gradient least squares (CGLS) method is proposed. Experimental results for the both synthetic and realistic low resolution (LR) SAR images demonstrate that the proposed method achieves the state-of-the-art performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 55, August 2018, Pages 853-865
Journal: Journal of Visual Communication and Image Representation - Volume 55, August 2018, Pages 853-865
نویسندگان
Naser Karimi, Mohammad Reza Taban,