基于轮廓波维纳滤波的图像压缩传感重构

图像压缩传感重构利用自然图像可稀疏表示的先验知识,从比奈奎斯特采样率低得多的随机投影观测值中重构原始图像.为了克服传统的压缩传感重构中正交小波方向选择性差和未利用变换系数的邻域统计特性的缺点,利用了轮廓波维纳滤波去噪算子替代迭代阈值法中的阈值算子,进而提出了基于轮廓波维纳滤波的图像压缩传感的重构算法.实验结果表明,该算法提高了重构图像的峰值信噪比和视觉效果,保护了图像的细节,加快了重构算法的收敛速度.

Image compressed sensing reconstruction based on contourlet Wiener filtering

Image compressed sensing reconstruction uses the sparse prior of the natural image and can reconstruct the original image from far fewer measurements of random projection than Nyquist sampling rate. To overcome the poor directional selectivity of orthogonal wavelet bases and not exploiting the statistic characteristics of transform coefficients in neighborhood, the contourlet Wiener filtering denoising operator is used to replace the threshold operator in iterative thresholding algorithm. And then a reconstruction algorithm is proposed, which combines contourlet with Wiener filtering. Experiment results show that the proposed algorithm improves the peak signal-to-noise ratio and visual quality, protects image details, and accelerates the convergence of reconstruction algorithm.