一种分块自适应压缩感知图像重构算法
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摘要
CS理论中,在离散余弦变换下使用OMP算法重构图像时需要较高的测量值可以获得较好的重构效果,但是存在重构图像模糊的问题。为此,提出了基于离散余弦变换的图像分块自适应正交匹配追踪(BAD-OMP)算法。基于分块压缩感知技术,对图像进行均匀分块处理,根据图像块稀疏性进行自适应采样,再用均值滤波算法平滑处理,从而减少重构所需的测量值,降低块效应。仿真结果表明,采样率取0.1~0.35时,BAD-OMP算法重构图像的PSNR值较OMP算法的PSNR值高9~11 d B,实现了在低采样率下获得较高的重构质量。
In the CS theory,the use of OMP algorithm to reconstruct the image under discrete cosine transform requires a higher measurement value to obtain a better reconstruction effect,but there is a problem of reconstructing the image blur. In this paper,a Block-based Adaptive based on Discrete cosine transform with OMP( BAD-OMP) algorithm is proposed. Based on the block compression sensing technique,the image is uniformly punctured,and the adaptive sampling is carried out according to the sparseness of the image block,and the mean filtering algorithm is used to smooth the processing,thus reducing the required measurement value and reducing the blocking artifacts. The simulation results show that the PSNR value of the reconstructed image of BAD-OMP algorithm is 9 ~ 11 d B higher than the PSNR value of OMP algorithm,and the higher reconstruction quality is achieved at low sampling rate.
In the CS theory,the use of OMP algorithm to reconstruct the image under discrete cosine transform requires a higher measurement value to obtain a better reconstruction effect,but there is a problem of reconstructing the image blur. In this paper,a Block-based Adaptive based on Discrete cosine transform with OMP( BAD-OMP) algorithm is proposed. Based on the block compression sensing technique,the image is uniformly punctured,and the adaptive sampling is carried out according to the sparseness of the image block,and the mean filtering algorithm is used to smooth the processing,thus reducing the required measurement value and reducing the blocking artifacts. The simulation results show that the PSNR value of the reconstructed image of BAD-OMP algorithm is 9 ~ 11 d B higher than the PSNR value of OMP algorithm,and the higher reconstruction quality is achieved at low sampling rate.
引文
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[2]TROPP J A,GILBERT A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Trans.Information Theory,2007,53(12):4655-4666.
[3]GAN L.Block compressed sensing of natural images[C].Digital Signal Processing,2007 15thInternational Conference eon.IEEE,2007:403-406.
[4]沈明欣,刘文波.基于压缩感知理论的图像重构技术[J].电子科技,2011,24(3):9-12.
[5]马小薇.基于压缩感知的OMP图像重构算法改进[J].电子科技,2015,28(4):51-58.
[6]王威,杨蔚蔚,李正臣.基于DCT扇形划分的压缩感知图像处理[J].计算机工程与应用,2015,51(24):186-189.
[7]SONG B C,JEONG S C,CHOI Y.Video super-resolution algorithm using bi-directional overlapped block motion compensation and on-the-fly dictionary training[J].Circuits and Systems for Video Technology,IEEE Transactions on,2011,21(3):274-285.
[8]KAREN E,ALESSANDRO F,VLADIMIR K.Compressed sensing image reconstruction via recursive spatially adaptive filtering[C].IEEE International Conference on Image Processing,2017:549-552.
[9]李佳,高志荣,熊承义,等.加权结构组稀疏表示的图像压缩感知重构[J].通信学报,2017,38(2):196-202.
[10]FOWLER J E,MUN S,TRAMEL E W.Block-based compressed sensing of images and video[J].Foundations and Trends in Signal Processing,2012,4(4):297-416.