Compressed sensing of color images

Majumdar, Angshul; Ward, Rabab

doi:10.1016/j.sigpro.2010.05.016

Cited by 102 publications

(70 citation statements)

References 11 publications

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“…The proposed GIHT method not only pursues the sparsity of image in a given transform domain, but also takes into account image characteristics. As a result, quality of reconstructed image is improved in comparison to GSL20 algorithm [5] .…”

Section: ⅲ Proposed Schemementioning

confidence: 99%

“…Most of the solutions work on improving the quality of CS recovery for gray-scale image, and only few approaches focus on color images [5～7] . In [5], author proved that CS recovery solution for gray-scale images does not work well for color images since the correlation between three RGB color channels are not properly exploited.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…In order to overcome this problem, the work [5] proposed a group-sparsity minimization for exploiting the high correlation between color channels. That is, a high-value in one channel most likely means that the other two channel values are also high.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…However, the work [5] only focuses on pursuing sparsity of image in transform domain, but does not reflect image characteristics (e.g., energy distribution, structure of images) well. In order to overcome this problem, we employed a visually weighting process to the group-sparsity minimization for addressing human visual system characteristics [9] .…”

Section: ⅰ Introductionmentioning

confidence: 99%

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Smoothed Group-Sparsity Iterative Hard Thresholding Recovery for Compressive Sensing of Color Image

Nguyên¹,

Dinh²,

Trinh³

et al. 2014

Journal of the Institute of Electronics and Information Enginee

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Compressive sensing is a new signal acquisition paradigm that enables sparse/compressible signal to be sampled under the Nyquist-rate. To fully benefit from its much simplified acquisition process, huge efforts have been made on improving the performance of compressive sensing recovery. However, concerning color images, compressive sensing recovery lacks in addressing image characteristics like energy distribution or human visual system. In order to overcome the problem, this paper proposes a new group-sparsity hard thresholding process by preserving some RGB-grouped coefficients important in both terms of energy and perceptual sensitivity. Moreover, a smoothed group-sparsity iterative hard thresholding algorithm for compressive sensing of color images is proposed by incorporating a frame-based filter with group-sparsity hard thresholding process. In this way, our proposed method not only pursues sparsity of image in transform domain but also pursues smoothness of image in spatial domain. Experimental results show average PSNR gains up to 2.7dB over the state-of-the-art group-sparsity smoothed recovery method.

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Section: ⅲ Proposed Schemementioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smoothed Group-Sparsity Iterative Hard Thresholding Recovery for Compressive Sensing of Color Image

Nguyên¹,

Dinh²,

Trinh³

et al. 2014

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

show abstract

“…The joint-structured sparsity, on the other hand, could be employed by multispectral or hyperspectral images according to the high correlation or spectral redundancy between different bands. In fact, Majumdar and Ward [19] have used it for compressed sensing of color images. Therefore, when the structural information of the representation coefficients is integrated into sparse reconstruction, the solution is expected to be better than the 1 -norm optimization, which will, in turn, improve the performance of spatiotemporal fusion algorithms.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi‐channel multi‐contrast MRI acquisitions

et al. 2020

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Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

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Compressed sensing of color images

Cited by 102 publications

References 11 publications

Smoothed Group-Sparsity Iterative Hard Thresholding Recovery for Compressive Sensing of Color Image

Smoothed Group-Sparsity Iterative Hard Thresholding Recovery for Compressive Sensing of Color Image

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi‐channel multi‐contrast MRI acquisitions

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