PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems

Shi, Baoshun; Lian, Qiusheng; Fan, Xiaoyu

doi:10.1016/j.sigpro.2019.04.013

Cited by 14 publications

(10 citation statements)

References 33 publications

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“…In addition to the proposed SSBA algorithm, SBL [36], S-TLS [26], and R-FOCUSS [24] are also simulated to demonstrate the superior performance of SSBA. SBL reconstructs the target without the model errors.…”

Section: Simulations and Discussionmentioning

confidence: 99%

“…By contrast, RCI can also be modeled as a sparse reconstruction [21][22][23] or CS problem by exploiting the sparse prior of the target since RCI can be formed as a linear inverse problem [1,24] where the reference matrix is a random matrix. us, RCI with model error can be regarded as a perturbed CS problem where the sensing matrix is completely perturbed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Fan¹,

Zhou

Chen³

et al. 2020

Mathematical Problems in Engineering

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Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

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Section: Simulations and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Fan¹,

Zhou

Chen³

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Super-resolution models based on regular terms can be solved by the alternating direction method of multipliers (ADMM) algorithm [50]. In recent years, many scholars have proposed various algorithms based on the classic ADMM, such as the plug-and-play (PnP) ADMM [51,52,53,54,55] and and regularization by denoising (RED) framework [56,57,58,59]. They are powerful image-recovery frameworks that aim to minimize an explicit regularization objective constructed from a plug-in image-denoising function.…”

Section: Introductionmentioning

confidence: 99%

Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

Liu

Chen

Peng

et al. 2019

Sensors

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Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.

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“…Computational imaging (CI) and ghost imaging (GI) with single-pixel detectors are hot topics in the imaging field and have attracted increasing attention over the past two decades [1][2][3][4][5]. Although the origins of CI and GI are different, the imaging approaches are similar and closely related [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Diffraction-field-adaptive computational imaging for static and moving targets

Wu¹,

Wang²,

Wang³

et al. 2019

J. Opt.

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Single-pixel computational imaging (SPCI) reconstructs a target image from multimeasurements of the correlation between the target and a series of speckle masks. Accurate acquisition of diffraction fields plays an essential role in image formation of SPCI. Loss and distortions in diffraction fields generally lead to the deterioration of the image quality or the failure of imaging. Here, we propose a diffraction-field-adaptive method that provides highquality image reconstruction for SPCI. In particular, we introduce an adaptive method to accurately calculate the diffraction field in the reference path, where the calculation width of the diffraction field can change itself automatically to make the sampling satisfy Nyquist sampling theorem. Whether the target moves or keeps static, the diffraction field can be accurately obtained and the distortions during light transmission can be reduced by the proposed method. A SPCI model is established and three numerically experiments are conducted to verify the performance of the model. The results demonstrate that the proposed method can avoid diffraction field loss and distortions in the field calculations, and provides excellent imaging performance for both static and moving targets.

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PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems

Cited by 14 publications

References 33 publications

Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

Diffraction-field-adaptive computational imaging for static and moving targets

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