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Nonconvex Compressed Sampling of Natural Images and Applications to Compressed MR Imaging

机译：自然图像的非凸压缩采样及其在MR压缩图像中的应用

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There have been proposed several compressed imaging reconstruction algorithms for natural and MR images. In essence, however, most of them aim at the good reconstruction of edges in the images. In this paper, a nonconvex compressed sampling approach is proposed for structure-preserving image reconstruction, through imposing sparseness regularization on strong edges and also oscillating textures in images. The proposed approach can yield high-quality reconstruction as images are sampled at sampling ratios far below the Nyquist rate, due to the exploitation of a kind of approximateℓ0seminorms. Numerous experiments are performed on the natural images and MR images. Compared with several existing algorithms, the proposed approach is more efficient and robust, not only yielding higher signal to noise ratios but also reconstructing images of better visual effects.
机译：已经提出了几种用于自然和MR图像的压缩成像重建算法。但是，本质上，它们中的大多数都旨在对图像中的边缘进行良好的重建。本文提出了一种非凸压缩采样方法，该方法通过在强边缘上施加稀疏正则化以及振动图像中的纹理来进行结构保留图像的重建。由于采用了一种近似ℓ0的半真值，所提方法可以产生高质量的重建图像，因为图像的采样率远低于奈奎斯特速率。对自然图像和MR图像进行了大量实验。与现有的几种算法相比，所提出的方法更加有效和健壮，不仅产生了更高的信噪比，而且还重建了具有更好视觉效果的图像。

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