融合图像块低维流形特性与解析轮廓波稀疏性的压缩成像算法

基于图像的整体稀疏表示和图像块的局部特性，融合图像块低维流形特性和整幅图像在解析轮廓波表示下的稀疏性两种先验知识，该文提出了一种高质量压缩成像算法。该算法利用迭代硬阈值法和流形投影法重构图像。为减小运算复杂度，该文用多个线性子流形的并集来近似表示包含所有图像块的非线性流形，并根据图像块的主方向进行初始分类后再用稀疏正交变换获得各线性子空间的基。实验结果表明，该文算法的重构图像在峰值信噪比和视觉效果两方面均有显著提高。

Compressive Imaging Algorithm Combined the Low Dimensional Manifold Property of Image Patch with the Sparse Representation of Analytic Contourlet

Based on global sparse representation of image and local property of the patch, an efficient compressive imaging algorithm is proposed, which combined two priors: the low dimensional manifold property of local image patch and the sparse representation of analytic contourlet. The iterative hard threshold and manifold projection method are used to reconstruct images. To reduce the computational complexity, the union of a group of linear sub-manifolds is used to approximate the nonlinear manifold which tiling the whole space of patch. The initial classification is obtained based on the dominant orientation of the local image patch, then the base of every linear subspace is obtained by sparse orthogonal transform over the blocks corresponding to each class. Experimental results show that the proposed algorithm can reconstruct an image more efficiently both in the Peak Signal-to-Noise Ratio (PSNR) and visual quality than the current algorithms.