A fast image recovery using compressive sensing technique with block based Orthogonal Matching Pursuit

Sermwuthisarn, Parichat; Auethavekiat, Supatana; Patanavijit, Vorapoj

doi:10.1109/ispacs.2009.5383863

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…And finally, all reconstructed blocks are transformed back to spatial domain. Compared to our previous work [9], this technique provided images with comparable quality at much higher sparsity (less data required for reconstruction).…”

Section: Introductionmentioning

confidence: 85%

“…Block processing is one of the means to apply CS in large sized data. Block processing can be applied in two manners: 1) spatial block processing [4][5][6][7][8][9] and 2) sparse-domain block processing [10]. In spatial domain block processing, an input image (in dense domain) is divided into blocks of n×n pixels.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], the authors adopted the paradigm of block-based random image sampling coupled with the projection-based reconstruction which promotes sparsity and smoothness. In [9], it was found that there was no distinct difference between non-overlapped and overlapped block processing in small image. From our investigation, in large image, overlapped block reduced the blocking artifacts at the expense of the larger file size.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High frequency preserving fast compressive sensing based on wavelet block orthogonal matching pursuit

Sermwuthisarn

Auethavekiat

Patanavijit

2010

2010 10th International Symposium on Communications and Information Technologies

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Due to large data set, block processing is usually applied for fast compressive sensing (CS) reconstruction; however, it gives the undesired blocking artifact in reconstructed data. In order to reduce blocking artifact and preserve high frequency, this paper proposes a novel block processing on wavelet domain instead of spatial domain. No post-processing nor special mapping is included. CS is applied only on blocks where the data are really sparse. An enhancing process, often included for artifact reduction, is no longer necessary. Our algorithm was evaluated by reconstructing three standard images (Lena, Mandrill and Peppers) and then compared with scrambled block hadamard ensemble (SBHE) and block-based CS sampling with a smoothed PL variant using directional discrete wavelet transform (BCS-SPL-DDWT). In the experiment, it provided better reconstruction both objectively (PSNR) and subjectively at low measurement rate. It gave the sharpest image in all cases. Details were preserved and blocking artifact was not detectable.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High frequency preserving fast compressive sensing based on wavelet block orthogonal matching pursuit

Sermwuthisarn

Auethavekiat

Patanavijit

2010

2010 10th International Symposium on Communications and Information Technologies

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“…(19) The number of 2 optimization in DCS -SOMP = L k t=1 ( 2 optimization for t variables) (20) From (15) to (18), it can be concluded that the computational cost of OMP-PKS+RS is approximately pL times the cost of OMP-PKS. From (13), (14), (19) and (20), it can be concluded that the computational cost of DCS-SOMP is pL times the cost of OMP. Since both OMP-PKS+RS and DCS-SOMP reconstruct the ensemble of signals, their computational costs are higher than OMP and OMP-PKS.…”

Section: Appendix 1: Computational Costs Of Omp Omp-pks Omp-pks+rsmentioning

confidence: 99%

“…The incoherent bases are called the measurement vectors. CS has a wide range of applications including radar imaging [4], DNA microarrays [5], image reconstruction and compression [6][7][8][9][10][11][12][13][14], etc.…”

Section: Introductionmentioning

confidence: 99%

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

Sermwuthisarn

Auethavekiat

Gansawat

et al. 2012

EURASIP J. Adv. Signal Process.

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The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

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Compressive Sensing Image Coding with Perceptual Weighting Measuring Matrix

Song

Wang

Shang

et al. 2012

Communications in Computer and Information Science

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A fast image recovery using compressive sensing technique with block based Orthogonal Matching Pursuit

Cited by 18 publications

References 7 publications

High frequency preserving fast compressive sensing based on wavelet block orthogonal matching pursuit

High frequency preserving fast compressive sensing based on wavelet block orthogonal matching pursuit

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

Compressive Sensing Image Coding with Perceptual Weighting Measuring Matrix

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