基于结构组全变分模型的图像压缩感知重建

Image Compressed Sensing Reconstruction Based on Structural Group Total Variation

针对基于传统全变分(TV)模型的图像压缩感知(CS)重建算法不能有效地恢复图像的细节和纹理,从而导致图像过平滑的问题,该文提出一种基于结构组全变分(SGTV)模型的图像压缩感知重建算法。该算法利用图像的非局部自相似性和结构稀疏特性,将图像的重建问题转化为由非局部自相似图像块构建的结构组全变分最小化问题。算法以结构组全变分模型为正则化约束项构建优化模型,利用分裂Bregman迭代将算法分离成多个子问题,并对每个子问题高效地求解。所提算法很好地利用了图像自身的信息和结构稀疏特性,保护了图像细节和纹理。实验结果表明,该文所提出的算法优于现有基于全变分模型的压缩感知重建算法,在PSNR和视觉效果方面取得了显著提升。

To solve the problem that the traditional Compressed Sensing(CS)algorithm based on Total Variation(TV)model can not effectively restore details and texture of image,which leads to over-smoothing of reconstructed image,an image Compressed Sensing(CS)reconstruction algorithm based on Structural Group TV(SGTV)model is proposed.The proposed algorithm utilizes the non-local self-similarity and structural sparsity of image,and converts the CS recovery problem into the total variation minimization problem of the structural group constructed by non-local self-similar image blocks.In addition,the optimization model of the proposed algorithm is built with regularization constraint of the structural group total variation model,and it uses the split Bregman iterative algorithm to separate it into multiple sub-problems,and then solves them respectively.The proposed algorithm makes full use of the information and structural sparsity of image to protects the image details and texture.The experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art total variation based algorithm in both PSNR and visual perception.