Block Compressed Sensing of Images Using Directional Transforms

Mun, Sungkwang; Fowler, James E.

doi:10.1109/dcc.2010.90

Cited by 198 publications

(211 citation statements)

References 13 publications

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“…The extra data to encode the permutation information for different methods are tabulated in Table 3. As can be seen from the table, the proposed method can reduce the amount of extra data significantly compared with the former permutation Table 2 Comparison of the maximum block sparsity levels for different permutation schemes The smoothed projected Landweber [9] reconstruction algorithm is used for image reconstruction in Scheme (1). In Scheme (2), Scheme (3) and Scheme (4), the reconstruction is implemented by using IHT algorithm in block-by-block manner, which is fast and effective.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYBlock compressed sensing (CS) with optimal permutation is a promising method to improve sampling efficiency in CS-based image compression. However, the existing optimal permutation scheme brings a large amount of extra data to encode the permutation information because it needs to know the permutation information to accomplish signal reconstruction. When the extra data is taken into consideration, the improvement in sampling efficiency of this method is limited. In order to solve this problem, a new optimal permutation strategy for block CS (BCS) is proposed. Based on the proposed permutation strategy, an improved optimal permutation based BCS method called BCS-NOP (BCS with new optimal permutation) is proposed in this paper. Simulation results show that the proposed approach reduces the amount of extra data to encode the permutation information significantly and thereby improves the sampling efficiency compared with the existing optimal permutation based BCS approach.

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Section: Simulation Resultsmentioning

confidence: 99%

“…2. We consider the following configurations: (1) traditional BCS [9]; (2) BCS with random permutation (BCS-RP); (3) BCS-OP; (4) BCS-NOP. The re-orthogonalizing Gaussian matrix is used for sensing matrix.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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show abstract

“…The contributions of the present paper include 1) the development of a fast yet effective 2D reconstruction for MRI using a projected Landweber (PL) [10] framework wherein a directional transform and statistical thresholding are incorporated; and 2) the incorporation into this PL reconstruction of ME/MC based on a telescopic search that aids block matching and results in improved residual reconstruction for dynamic MRI. We refer to the resulting algorithm as motion-compensated CS with PL reconstruction (MC-CS-PL).…”

Section: Introductionmentioning

confidence: 99%

“…One effective method for CS reconstruction of x from measurements y is a PL algorithm [10,[12][13][14], which has also been called "iterative thresholding" (e.g., [15,16]). PL starts from some initial approximationx (0) and forms the approximation at iteration i + 1 asx…”

Section: Introductionmentioning

confidence: 99%

Motion-compensated compressed-sensing reconstruction for dynamic MRI

Mun

Fowler

2013

2013 IEEE International Conference on Image Processing

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“…In this paper, we adopt the framework of block compressed sensing(BCS) [11]. In this framework, the original image is divided into a certain number of blocks depending on rows and columns of the image and each block is sampled independently using the same measurement operator matching the size of the blocks.…”

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confidence: 99%

The Application of Wavelet-Based Contourlet Transform on Compressed Sensing

Zhao

et al. 2012

Multimedia and Signal Processing

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Abstract. Reasonable sparse representation of signals are one of the key factors to ensure the quality of compressed sampling, so a proper sparse representing methods should be selected to make the signals sparse to the greatest extent in the applications of compressed sensing. In this paper we adopted the framework of block compressed sensing to sample the images, used the iterative hard thresholding(IHT) algorithm to reconstruct the original images, and employed the wavelet-based contourlet transform, an improved contourlet transform, to decompose 2D images in IHT reconstruction process. Numerical experiments indicated that the runtime of the reconstruction algorithm adopting waveletbased contourlet transform is the shortest compared to that adopting contourlet transform and that adopting wavelet transform; under low compression ratios, the quality of the reconstructed images using wavelet-based contourlet transform is superior to that using contourlet transform and that using traditional wavelet transform.

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Block Compressed Sensing of Images Using Directional Transforms

Cited by 198 publications

References 13 publications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Motion-compensated compressed-sensing reconstruction for dynamic MRI

The Application of Wavelet-Based Contourlet Transform on Compressed Sensing

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