An Image Reconstruction Algorithm based on Sparse Representation for Image Compressed Sensing

It is difficult to control the balance between artifact suppression and detail preservation. In addition, the information contained in the reconstructed image is limited. For achieving the purpose of less lost information and lower computational complexity in the sampling process, this paper proposed a novel algorithm to realize the image reconstruction using sparse representation. Firstly, the principle of algorithm for sparse representation is introduced, and then the current commonly used reconstruction algorithms are described in detail. Finally, the algorithm can still process the image when the sparsity is unknown by introducing the sparsity theory and dynamically changing the step size to approximate the sparsity. The results explain that the improved algorithm can not only reconstruct the image with unknown sparsity, but also has advantages over other algorithms in reconstruction time. In addition, compared with other algorithms, the reconstruction time of the improved algorithm is the shortest under the same sampling rate.

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Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Applied Sciences ◽

10.3390/app11041435 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1435

Author(s):

Xue Bi ◽

Lu Leng ◽

Cheonshik Kim ◽

Xinwen Liu ◽

Yajun Du ◽

...

Keyword(s):

Image Reconstruction ◽

Compressed Sensing ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Energy Criterion ◽

Step Size ◽

Relationship Analysis ◽

Support Set ◽

Reconstruction Performance ◽

Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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

BioMed Research International ◽

10.1155/2017/6753831 ◽

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.

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Patch-based sparse reconstruction for electrical impedance tomography

Sensor Review ◽

10.1108/sr-07-2016-0126 ◽

2017 ◽

Vol 37 (3) ◽

pp. 257-269 ◽

Cited By ~ 4

Author(s):

Qi Wang ◽

Pengcheng Zhang ◽

Jianming Wang ◽

Qingliang Chen ◽

Zhijie Lian ◽

...

Keyword(s):

Image Reconstruction ◽

Sparse Representation ◽

Electrical Impedance Tomography ◽

Electrical Impedance ◽

Inverse Operator ◽

Reconstruction Algorithm ◽

Impedance Tomography ◽

Content Type ◽

Ill Posed ◽

High Level

Purpose Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. Design/methodology/approach This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity. Findings Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Originality/value EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

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Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

Journal of X-Ray Science and Technology ◽

10.3233/xst-211069 ◽

2022 ◽

pp. 1-13

Author(s):

Lei Shi ◽

Gangrong Qu ◽

Yunsong Zhao

Keyword(s):

Image Reconstruction ◽

Condition Number ◽

Reconstruction Algorithm ◽

Projection Data ◽

Ct Image ◽

Reconstruction Algorithms ◽

Edge Preserving ◽

Image Reconstruction Algorithm ◽

Ct Image Reconstruction ◽

Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

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Image reconstruction algorithm based on variable atomic number matching pursuit

Journal of Algorithms & Computational Technology ◽

10.1177/1748301816673074 ◽

2016 ◽

Vol 11 (2) ◽

pp. 103-109

Author(s):

Hongtu Zhao ◽

Chong Chen ◽

Chenxu Shi

Keyword(s):

Image Reconstruction ◽

Convex Optimization ◽

Atomic Number ◽

Matching Pursuit ◽

Greedy Algorithms ◽

Optimization Algorithms ◽

Reconstruction Algorithm ◽

Reconstruction Algorithms ◽

Reconstruction Quality ◽

Stable Reconstruction

As the most critical part of compressive sensing theory, reconstruction algorithm has an impact on the quality and speed of image reconstruction. After studying some existing convex optimization algorithms and greedy algorithms, we find that convex optimization algorithms should possess higher complexity to achieve higher reconstruction quality. Also, fixed atomic numbers used in most greedy algorithms increase the complexity of reconstruction. In this context, we propose a novel algorithm, called variable atomic number matching pursuit, which can improve the accuracy and speed of reconstruction. Simulation results show that variable atomic number matching pursuit is a fast and stable reconstruction algorithm and better than the other reconstruction algorithms under the same conditions.

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Super-resolution reconstruction for a single image based on self-similarity and compressed sensing

Journal of Algorithms & Computational Technology ◽

10.1177/1748301818778244 ◽

2018 ◽

Vol 12 (3) ◽

pp. 234-244

Author(s):

Qiang Yang ◽

Huajun Wang

Keyword(s):

Image Reconstruction ◽

Compressed Sensing ◽

Sparse Representation ◽

Super Resolution ◽

Reconstruction Algorithm ◽

Single Image ◽

Low Resolution ◽

Self Similarity ◽

Resolution Image ◽

Reconstruction Performance

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

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Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500310 ◽

2019 ◽

Vol 17 (05) ◽

pp. 1950031

Author(s):

Ashok Naganath Shinde ◽

Sanjay L. Lalbalwar ◽

Anil B. Nandgaonkar

Keyword(s):

Compressed Sensing ◽

Signal Reconstruction ◽

Sampling Rate ◽

Reconstruction Algorithm ◽

Haar Wavelet ◽

Evaluation Procedure ◽

Grey Wolf Optimizer ◽

Reconstruction Algorithms ◽

Swarm Optimization ◽

Glowworm Swarm Optimization

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

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A UWB-2PPM Reconstruction Algorithm without a Priori Knowledge of Pilot

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3545 ◽

2014 ◽

Vol 556-562 ◽

pp. 3545-3548

Author(s):

Hai Bo Yin ◽

Jun An Yang ◽

Jie Gong ◽

Wei Dong Wang

Keyword(s):

Theoretical Analysis ◽

A Priori ◽

Sampling Rate ◽

Reconstruction Algorithm ◽

A Priori Knowledge ◽

Reconstruction Algorithms ◽

High Sampling Rate ◽

The Common ◽

Transmitted Signal ◽

Complex Channel

Compressed Sensing is very efficient in reducing the relatively high sampling rate. But when it comes to the channel estimation of uncooperative communication, the common CS reconstruction algorithms seem impractical to implement since a pilot is required, which is difficult for uncooperative communication. In this paper, we combine the sparsity transform dictionary, which is formed by a sequence of delays of the template signal, together with the idea of alternative minimization to improve the traditional CoSaMP algorithm to reconstruct under-sampled UWB-2PPM signal transmitted by unkown complex channel without a knowledge of pilot. The theoretical analysis and simulations show that the proposed algorithm is capable of reconstructing the original transmitted signal without a pilot.

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Evaluation of 68Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

10.21203/rs.3.rs-30754/v1 ◽

2020 ◽

Author(s):

Manuel Weber ◽

Regina Hofferber ◽

Ken Herrmann ◽

Wolfgang Peter Fendler ◽

Maurizio Conti ◽

...

Keyword(s):

Prostate Cancer ◽

Image Reconstruction ◽

Diagnostic Performance ◽

Reconstruction Algorithm ◽

Reconstruction Algorithms ◽

Time Duration ◽

Acquisition Protocol ◽

Scan Time ◽

Psma Pet ◽

Pet Ct

Abstract Aim 68Ga-PSMA PET/CT allows for a superior detection of prostate cancer (PC) tissue, especially in context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand 68Ga-PSMA PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance. Methods Twenty PC patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent 68Ga-PSMA PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a scan time duration of approximately 17 min (reference acquisition protocol) and 5 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution. Results Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13–21% (4 iterations) and from 10–15% (2 iterations) for PSF + TOF and TOF images. Conclusions High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered 68Ga-PSMA activities can be considered in metastatic patients, where missing small lesions would not impact patient management.

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Image Reconstruction Algorithms For Ultrasonic Tomography

Jurnal Teknologi ◽

10.11113/jt.v54.818 ◽

2012 ◽

Cited By ~ 1

Author(s):

Mohd Hafiz Fazalul Rahiman ◽

Ruzairi Abdul Rahim ◽

Herlina Abdul Rahim

Keyword(s):

Image Reconstruction ◽

Reconstruction Algorithm ◽

Ultrasonic Tomography ◽

Back Projection ◽

Reconstruction Algorithms ◽

Tomography System ◽

Tomography Image ◽

Real Objects ◽

Linear Back Projection ◽

Image Reconstruction Algorithms

Kertas ini membincangkan algoritma pembangunan imej bagi kegunaan dalam tomografi ultrasonik. Terdapat tiga jenis algoritma pembangunan iaitu Linear Back Projection, Hybrid Reconstruction dan Hybrid Binary Reconstruction. Algoritma tersebut telah diuji ke atas sistem tomografi ultrasonik berdasarkan kepada beberapa bayang yang telah dikenalpasti dan objek–objek sebenar. Prestasi algoritma tersebut telah di analisa dan bincangkan pada bahagian akhir kertas ini. Kata kunci: Algoritma pembangunan; tomografi ultrasonic; pemprosesan image; mabuk This paper presented image reconstruction algorithms for use in ultrasonic tomography. There are three types of reconstruction algorithms namely Linear Back Projection, Hybrid Reconstruction and Hybrid Binary Reconstruction. The algorithms have been evaluated on ultrasonic tomography system based on several known phantoms and real objects. The performance of the algorithms have been analysed and discussed at the end of the paper. Key words: Reconstruction algorithm; ultrasonic tomography; image processing

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