CAJ | 학술논문

针对图像压缩采样中正交小波变换方向有限和单一正则化的问题，提出了一种基于Shearlet的双正则化图像压缩采样恢复算法。该算法用Shearlet作为图像的稀疏表示，用交替最小化对联合正则化模型进行求解。实验结果表明，该算法恢复的图像与单一的全变分正则化方法和小波变换相比有更好的视觉效果，更高的峰值信噪比。
침대도상압축채양중정교소파변환방향유한화단일정칙화적문제，제출료일충기우Shearlet적쌍정칙화도상압축채양회복산법。해산법용Shearlet작위도상적희소표시，용교체최소화대연합정칙화모형진행구해。실험결과표명，해산법회복적도상여단일적전변분정칙화방법화소파변환상비유경호적시각효과，경고적봉치신조비。
The orthogonal wavelet fails to provide an optimal sparse representation for images that contain texture details due to limited direction, and the current regularization method is singleness. In this paper, it proposes a compressed sensing reconstruction algorithm based on Shearlet sparse representation and compound regularizers, the algorithm uses Shearlet for image sparse representation, and the problem is solved by alternating minimization algorithm. The experimental results indicate that the visual quality and PSNR of reconstructed image is improved by proposed algorithm compared with single total variation regularizer method and wavelet transform.