scholarly journals Dual-Channel Reconstruction Network for Image Compressive Sensing

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2549 ◽  
Author(s):  
Zhongqiang Zhang ◽  
Dahua Gao ◽  
Xuemei Xie ◽  
Guangming Shi
Keyword(s):  
Compressive Sensing ◽  
Linear Mapping ◽  
Reconstructed Image ◽  
Reconstruction Algorithms ◽  
Dual Channel ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Reconstruction Quality ◽  
Under Sampling ◽  
Fully Connected

The existing compressive sensing (CS) reconstruction algorithms require enormous computation and reconstruction quality that is not satisfying. In this paper, we propose a novel Dual-Channel Reconstruction Network (DC-Net) module to build two CS reconstruction networks: the first one recovers an image from its traditional random under-sampling measurements (RDC-Net); the second one recovers an image from its CS measurements acquired by a fully connected measurement matrix (FDC-Net). Especially, the fully connected under-sampling method makes CS measurements represent original images more effectively. For the two proposed networks, we use a fully connected layer to recover a preliminary reconstructed image, which is a linear mapping from CS measurements to the preliminary reconstructed image. The DC-Net module is used to further improve the preliminary reconstructed image quality. In the DC-Net module, a residual block channel can improve reconstruction quality and dense block channel can expedite calculation, whose fusion can improve the reconstruction performance and reduce runtime simultaneously. Extensive experiments manifest that the two proposed networks outperform state-of-the-art CS reconstruction methods in PSNR and have excellent visual reconstruction effects.

Nonuniform Spatial Sampling in a Ground-Based Noise SAR

2011 ◽  
Vol 57 (1) ◽  
pp. 71-75 ◽  
Author(s):  
Łukasz Maślikowski ◽  
Jacek Misiurewicz
Keyword(s):  
Synthetic Aperture Radar ◽  
Compressive Sensing ◽  
Sampling Frequency ◽  
Nonuniform Sampling ◽  
Spatial Sampling ◽  
Synthetic Aperture ◽  
Reconstruction Algorithms ◽  
Spatial Aliasing ◽  
Reconstruction Methods ◽  
Along Track

Nonuniform Spatial Sampling in a Ground-Based Noise SARThe paper presents an idea of nonuniform spatial sampling applied to a noise synthetic aperture radar. In certain cases it is desirable to limit the number of spatial (along-track) domain samples acquired in a SAR radar because of external constraints on sampling frequency or on the overall number of samples - e.g. in order to economy on time or power consumed. Lowering number of samples taken may, however, lead to spatial aliasing and incorrect reconstruction of the image. Nonuniform sampling allows to reduce the aliasing effect and reconstruct the image better. This technique can be applied with standard reconstruction methods, but it works best together with Compressive Sensing reconstruction algorithms. The idea will be verified with an experimental noise SAR built at ISE PW.

scholarly journals GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Huiping Qin ◽  
Xiaojuan Xie
Keyword(s):  
Compressive Sensing ◽  
Estimation Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Estimation Methods ◽  
Signal Estimation ◽  
Reconstruction Algorithms ◽  
Modified Newton Method ◽  
Reconstruction Performance ◽  
Multipath Signal

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

scholarly journals Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 385
Author(s):  
Kunhao Zhang ◽  
Yali Qin ◽  
Huan Zheng ◽  
Hongliang Ren ◽  
Yingtian Hu
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Total Variation ◽  
Sampling Rate ◽  
Low Rank ◽  
Reconstructed Image ◽  
Total Variation Regularization ◽  
Edge Information ◽  
Reconstruction Performance ◽  
Bilateral Total Variation

The use of non-local self-similarity prior between image blocks can improve image reconstruction performance significantly. We propose a compressive sensing image reconstruction algorithm that combines bilateral total variation and nonlocal low-rank regularization to overcome over-smoothing and degradation of edge information which result from the prior reconstructed image. The proposed algorithm makes use of the preservation of image edge information by bilateral total variation operator to enhance the edge details of the reconstructed image. In addition, we use weighted nuclear norm regularization as a low-rank constraint for similar blocks of the image. To solve this convex optimization problem, the Alternating Direction Method of Multipliers (ADMM) is employed to optimize and iterate the algorithm model effectively. Experimental results show that the proposed algorithm can obtain better image reconstruction quality than conventional algorithms with using total variation regularization or considering the nonlocal structure of the image only. At 10% sampling rate, the peak signal-to-noise ratio gain is up to 2.39 dB in noiseless measurements compared with Nonlocal Low-rank Regularization (NLR-CS). Reconstructed image comparison shows that the proposed algorithm retains more high frequency components. In noisy measurements, the proposed algorithm is robust to noise and the reconstructed image retains more detail information.

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

scholarly journals Evaluation of Algebraic Iterative Image Reconstruction Methods for Tetrahedron Beam Computed Tomography Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Joshua Kim ◽  
Huaiqun Guan ◽  
David Gersten ◽  
Tiezhi Zhang
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Cone Angle ◽  
Iterative Algorithms ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Large Cone Angle ◽  
Using Data ◽  
Dual Detector

Tetrahedron beam computed tomography (TBCT) performs volumetric imaging using a stack of fan beams generated by a multiple pixel X-ray source. While the TBCT system was designed to overcome the scatter and detector issues faced by cone beam computed tomography (CBCT), it still suffers the same large cone angle artifacts as CBCT due to the use of approximate reconstruction algorithms. It has been shown that iterative reconstruction algorithms are better able to model irregular system geometries and that algebraic iterative algorithms in particular have been able to reduce cone artifacts appearing at large cone angles. In this paper, the SART algorithm is modified for the use with the different TBCT geometries and is tested using both simulated projection data and data acquired using the TBCT benchtop system. The modified SART reconstruction algorithms were able to mitigate the effects of using data generated at large cone angles and were also able to reconstruct CT images without the introduction of artifacts due to either the longitudinal or transverse truncation in the data sets. Algebraic iterative reconstruction can be especially useful for dual-source dual-detector TBCT, wherein the cone angle is the largest in the center of the field of view.

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

2014 ◽  
Vol 989-994 ◽  
pp. 3946-3951
Author(s):  
Xin Jin ◽  
Ming Feng Jiang ◽  
Jie Feng
Keyword(s):  
Compressed Sensing ◽  
Group Structure ◽  
Direct Method ◽  
Group Sparsity ◽  
Mr Image ◽  
Assignment Method ◽  
Group Assignment ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Space Data

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

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