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- W2380255976 abstract "Constrained by traditional sampling theory,it's difficult to obtain high resolution images from signal acquisition systems and great amounts of date occur as a result.The theory of compressed sensing broke through the Nyquist sampling theory by combining sampling and compressing together under the assumption that the signal is compressible or sparse in a certain sparse transform domain.Traditional image reconstruction algorithms based on the compressed sensing theory measure all the coefficients,which needs multi-layer wavelet transform to ensure image quality.Since the wavelet can only capture limited direction information,the reconstructed image is consequently of low resolution.Nonsubsampled contourlet transform was thus introduced as the sparse transform.According to the properties of coefficients,only the high-pass coefficients were measured,then the original image was reconstructed using the orthogonal matching pursuit method.Compared with the original algorithm,simulation results show that high resolution images can be obtained with only one layer nonsubsampled contourlet transform,thus reducing the sampling and storage data and greatly improving the quality of the reconstruction image." @default.
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- W2380255976 date "2012-01-01" @default.
- W2380255976 modified "2023-09-24" @default.
- W2380255976 title "Compressed sensing based on nonsubsampled contourlet transform for image reconstruction" @default.
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