Hyperspectral Image Denoising with Composite Regularization Models

Li, Ao; Chen, Deyun; Lin, Kezheng; Sun, Guanglu

doi:10.1155/2016/6586032

Cited by 5 publications

(3 citation statements)

References 37 publications

(44 reference statements)

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“…In MRI imaging, CS-MRI related research has received more and more attention and has potential significance in accelerating imaging [7]. Motivated by successful achievements of regularization-based techniques in image reconstruction issues [8][9][10], many regularization-based methods have been studied in recent years. Michael Lustig et al applied CS to MRI for the first time, established the entire model, and proposed the use of the non-linear conjugate gradient algorithm to solve this optimization problem.…”

Section: Previous Workmentioning

confidence: 99%

Tensor Analysis-based Nonlocal Magnetic Resonance Image Reconstruction with Compressed Sensing

Li¹

2018

IJPE

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Compressed sensing (CS) is a novel and important technique in MRI reconstruction, which can be used to reconstruct magnetic resonance images with few sampled data while simultaneously speeding up imaging. The conventional CS-based MRI is implemented from a global view, which leads to some disadvantages: it not only loses many local structures but also fails to preserve detail information. To obtain better reconstruction quality, we propose a novel CS-based reconstruction model, which is incorporated with nonlocal technology to allow for the preservation of extra details. The proposed model groups similar patches within the nonlocal area and stacks them to form a 3D array unit. Then, to process the array in a realistic 3D manner, a tensor-based sparsity constraint is developed as the regularization on the reconstructed image. Experimental results show that the performance of the proposed method is superior to those of conventional methods.

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Section: Previous Workmentioning

confidence: 99%

Tensor Analysis-based Nonlocal Magnetic Resonance Image Reconstruction with Compressed Sensing

Li¹

2018

IJPE

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“…Composite regularization models have an extensive use not only in sparse signal processing, but also in variety fields using computational imaging [13]- [15]. In all application scenarios, regularization parameters control the effect of corresponding penalty terms and can greatly influence the performance of regularization models.…”

Section: Introductionmentioning

confidence: 99%

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

Fan,

Wang,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in sparse microwave imaging, including ℓ1 and ℓ2 penalty, nonconvex and TV penalty, combined dictionary, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method.

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“…A compressed sensing approach for non‐local sparse representation of 3D data cube to exploit spatial and spectral information in coded aperture snapshot spectral imaging is explored in [15]. Similarly, a patch‐based sparse representation technique to maintain correlation along a local spectral dimension and a non‐local technique to obtain spatial correlation is proposed in [16]. The resultant non‐convex problem is solved using the alternating direction method of multipliers (ADMM).…”

Section: Introductionmentioning

confidence: 99%