کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562627 875419 2013 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multivariate pursuit image reconstruction using prior information beyond sparsity
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Multivariate pursuit image reconstruction using prior information beyond sparsity
چکیده انگلیسی

The prior information of images plays an important role in reducing the computational complexity of CS inversion and improving the reconstruction quality. A wavelet-based multivariate pursuit algorithm, which exploits the prior information of images that goes beyond simple sparsity, is developed in this paper. The proposed method reconstructs the image wavelet coefficients from the multiple measurements in a multivariate manner, and uses the extracted image edge as the prior information to guide the pursuit process of algorithm in CS recovery. By means of the interaction of edge information and multivariate joint recovery, the proposed algorithm significantly improves the reconstruction quality of those images with the obvious edges and high sparsity, such as CT, MRI images. Numerical experiments demonstrate that the proposed algorithm returns superior reconstructed quality and remains higher computational efficiency than other state-of-the-art CS algorithms.


► The wavelet-based CS reconstruction is converted to a jointly sparse recovery.
► The extracted edge is used as the prior for image CS reconstruction.
► A fast edge-based multivariate pursuit algorithm (EMPA) is proposed.
► The interaction of edge and joint recovery contributes a lot to reconstruction.
► EMPA has shown a good performance under low CS measurement rates.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 93, Issue 6, June 2013, Pages 1662–1672
نویسندگان
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