【摘要】 在图像分块压缩感知(Block compressed sensning,BCS)框架下,基于平滑投影Landweber迭代的重建算法能以低计算复杂度确保良好率失真性能,尤其是采用主成分分析(Principle component analysis,PCA)作自适应硬阈值收缩。然而,在PCA学习过程中忽略了图像局部结构特性平稳,会影响Landweber迭代重建性能的提升。针对该问题,本文采用粒计算(Granular computing,Gr C)理论,根据图像子块结构特性将图像分解为若干粒,再实施PCA学习各粒的稀疏表示基底,并对粒内子块硬阈值收缩去噪。由于粒内图像子块具有平稳的结构特性,可有效改善硬阈值收缩性能。实验结果表明,与传统算法相比,本文算法重建图像的整体客观质量较优,且可更好地保护边缘与纹理等重要细节,主观视觉质量良好,与此同时,保证了较低的重建计算复杂度。更多还原

【Abstract】 In the framework of block compressed sensing(BCS),the reconstruction algorithm based on the smoothed-projected Landweber iteration can achieve better performance of rate-distortion with a low computational complexity,especially for the case using the principle component analysis(PCA) to conduct adaptive hard-thresholding shrinkage. However,during learning PCA matrix,the reconstruction performance of Landweber iteration is affected because of neglecting the stationary local structural characteristic of image. To solve the above problems,the granular computing(GrC) is adopted to decompose an image into several granules depending on the structural features of patches,and then PCA is performed to learn the sparse representation basis corresponding to each granule. Finally,the hard-thresholding shrinkage is used to remove the noises in patches. The patches in granule have the stationary local structural characteristic,and the proposed method can thus effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has a better objective quality when comparing with several traditional ones,and its edge and texture details are better preserved,which guarantees the better subjective visual quality. Besides,the method has a low computational complexity of reconstruction.更多还原