基于加权L1最小化的图像小波域压缩感知重构

压缩感知理论因为能以少量的采样精确地重构原始信号而得到广泛关注.通过在压缩感知的框架下研究小波域图像重构问题，提出了一类小波域的加权l1最小化方法.该方法不仅利用了信号稀疏性的先验信息，而且在重构模型中，通过对不同小波子带上的系数施加不同的权重，从而整合了图像小波域的结构信息，与经典的压缩感知算法相比具有更好的信号可恢复性.仿真实验结果表明，选用该方法能够以更少的采样得到同等精度的重构图像，验证了该方法的有效性.

Weighted Minimization for Compressive Sensing Image Reconstruction in Wavelet Domain

Compressive sensing has received much attention in the signal processing field for it can reconstruct a signal or image from surprisingly few samples.In this paper,the author investigates the wavelet domain image reconstruction problem and proposes a weighted li minimization algorithm to reconstruct the images.The proposed method utilizes not only the sparsity of signals,but also incorporates the structure information of images in wavelet domain.Hence,compared with the classical compressive sensing algorithm,the proposed method has better recoverability.Simulation results show that the proposed method has achieved the same equality image from few samples,which demonstrates the validity of the proposed method.