基于压缩感知的超分辨率图像重建

Super-resolution image reconstruction algorithms based on compressive sensing

压缩感知(CS)利用图像稀疏表示的先验知识,从少量的观测值中重建出原始图像。将CS理论应用于单幅图像超分辨率(SR),提出一种基于两步迭代收缩算法和全变分(TV)稀疏表示的图像重建方法。该方法无需任何训练集,仅需单幅低分辨率实现图像重建。算法在测量矩阵里加入下采样低通滤波器以使SR问题满足应用CS理论的有限等距性质;采用TV正则化函数,利用两步迭代法引入TV去噪算子,可以更好地重建图像边缘。实验结果证明,与已有的超分辨率方法相比,在不同的放大倍数下所提方法重建图像视觉效果更好,在峰值信噪比(PSNR)的评价指标上有显著的提高(4～6 dB),且实验证实滤波器的引入决定算法的重建质量。

Compressed Sensing （CS） theory can reconstruct original images from fewer measurements using the priors of the images sparse representation. The CS theory was applied into the single-image Super-Resolution （SR）, and a new reconstruction algorithm based on two-step iterative shrinkage and Total Variation （TV） sparse representation was proposed. The proposed method does not need an existing training set but the single input low resolution image. A down-sampling low- pass filter was incorporated into measurement matrix to make the SR problem meet the restricted isometry property of CS theory, and the TV regularization method and a two-step iterative method with TV denoising operator were introduced to make an accurate estimate of the image＇s edge. The experimental results show that compared with the existing super-resolution techniques, the proposed algorithm has higher precision and better performance under different magnification level, the proposed method achieves significant improvement （ about 4 - 6 dB） in Peak Signal-to-Noise Ratio （ PSNR）, and the filter plays a decisive role in the reconstruction quality.