Compressed Sensing Image Reconstruction Algorithm Research Articles

Hybridizing ant lion with whale optimization algorithm for compressed sensing MR image reconstruction via l1 minimization: an ALWOA strategy

In the field of image compression, the compressed sensing image reconstruction has made great achievements due to proper use of the image sparsity without the Nyquist sampling law constraint. For image information distribution, great deals of researches indicate that there exists obvious structural and statistical prior’s regularity and by the traditional compression algorithm it is difficult to achieve. In this paper, we propose a compressed sensing image reconstruction algorithm based on hybrid ALWOA strategy, which combines the ant lion optimization algorithm and the whale optimization algorithm. The hybrid algorithm produces a global search with faster convergence. By continuously learning the proposed hybrid method can find optimal solutions. The objective function for the image reconstruction process is taken as the l1 minimization problem. The reconstructed image is obtained by solving the l1 minimization problem. Extensive simulations have been conducted and the results show that the proposed method has achieved better performance when compared with traditional reconstruction algorithms.

Hybrid-Weighted Total Variation and Nonlocal Low-Rank-Based Image Compressed Sensing Reconstruction

To reconstruct natural images from compressed sensing (CS) measurements accurately and effectively, a CS image reconstruction algorithm based on hybrid-weighted total variation (HWTV) and nonlocal low-rank (NLR) is proposed. It considers the local smoothness and nonlocal self-similarity (NSS) in image, improves traditional hybrid total variation (TV) model, and constructs a new edge detection operator with mean curvature to adaptively select the TV. The HWTV combines the advantages of first-order TV and second-order TV to preserve the edges of the image and avoid the staircase effect in the smooth areas. And NLR can effectively reduce the redundant information and retain the structural information of the image. In addition, the proposed algorithm constructs prior regularization terms with improved HWTV model and NLR model, and utilizes soft threshold function and smooth but non-convex function to solve the TV and low-rank optimization problems, respectively. Finally, the alternative direction multiplier method (ADMM) iterative strategy is used to separate the target model into several sub-problems, and the most efficient methods are adopted to solve each sub-problem. Experimental results show that, compared with the state-of-the-art CS reconstruction algorithms, the proposed algorithm can achieve higher reconstruction quality, especially in the case of low sampling rates.

Compressed Image Sensing by Jointly Leveraging Multi-Scale Heterogeneous Priors for the Internet of Multimedia Things

As an important branch of the Internet of Things, the Internet of Multimedia Things (IoMT) has complex application scenarios, thus putting forward new challenges to data communication. With the application of compressed sensing (CS)-based image compression and transmission in the field of IoMT, the traditional CS image reconstruction algorithms have been extended to model-based algorithms, which exploit more prior information of multimedia objects other than sparsity/compressibility in the reconstruction process. However, these algorithms mainly exploit just one type of prior information, still leaving room for further improvement. In this paper, we propose a novel CS image reconstruction algorithm by jointly leveraging multi-scale (local and global) heterogeneous (statistical and structural) priors of natural images, named jointly leveraging statistical and structural priors for CS image reconstruction (JLSSP-CS), to enable high-quality CS image recovery for IoMT. Specifically, the proposed JLSSP-CS algorithm is realized under the iterative hard thresholding framework, and the reconstruction process is composed of two phases. In the first phase, the local statistical correction and the global statistical correction are considered sequentially. Then, the global statistical and structural priors are exploited in nested iterations to further refine the recovery result in the second phase. The extensive simulations have been conducted, and the results indicate that the proposed JLSSP-CS algorithm outperforms the current state of the art by realizing high-quality image reconstruction with a small number of measurements for IoMT end devices.

Compressed sensing image reconstruction using intra prediction

Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality.

Ultrashort echo time (UTE) imaging using gradient pre-equalization and compressed sensing

Ultrashort echo time (UTE) imaging is a well-known technique used in medical MRI, however, the implementation of the sequence remains non-trivial. This paper introduces UTE for non-medical applications and outlines a method for the implementation of UTE to enable accurate slice selection and short acquisition times. Slice selection in UTE requires fast, accurate switching of the gradient and r.f. pulses. Here a gradient “pre-equalization” technique is used to optimize the gradient switching and achieve an effective echo time of 10μs. In order to minimize the echo time, k-space is sampled radially. A compressed sensing approach is used to minimize the total acquisition time. Using the corrections for slice selection and acquisition along with novel image reconstruction techniques, UTE is shown to be a viable method to study samples of cork and rubber with a shorter signal lifetime than can typically be measured. Further, the compressed sensing image reconstruction algorithm is shown to provide accurate images of the samples with as little as 12.5% of the full k-space data set, potentially permitting real time imaging of short T2* materials.

Medical Image Compressed Sensing Reconstruction

In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

Rapid Gradient Projection Algorithm for Image Reconstruction

In order to improve the speed of compressed sensing image reconstruction algorithm, a rapid gradient projection algorithm for image reconstruction is proposed. In traditional Gradient Projection algorithm, the pursuit direction is alternating, in rapid gradient projection algorithm, we use the Newton's method to calculate the gradient descent direction, thus the constraints conditions of gradient projection is satisfied. And the target function is updated in each iteration computing. The effect of approximation matrix to target function is reduced. The iteration computing times is reduced, because the algorithm works in accurate search direction. Experiment results show that, compared with the GPSR algorithm, the RGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

应用投影收缩的压缩感知锥束CT短扫描重建

To solve the problem of image reconstruction of incomplete projection data from a short-scan cone-beam CT, a novel cone-beam CT short-scan reconstruction algorithm was proposed based on projection-contraction method. Aiming at the non-monotonic convergence of the Gradient-Projection Barzilari-Borwein (GPBB) algorithm, the predictor-corrector feature of projection-contraction method was analyzed and incorporated into compressed sensing image reconstruction algorithm. The objective function descent direction and the projection onto convex sets descent direction were combined to correct the results of GPBB algorithm to improve the non-monotonic convergence of GPBB algorithm. Then, the experiments were conducted on simulated projection data and phantom scanning data. The simulated results for 25 sampling angles show that the signal-to-noise ratios of images reconstructed by PCBB algorithm are 9.4870, 9.8027, 3.6159 db higher than those of images reconstructed by Adaptive Steepest Descent-Projection onto Convex Sets algorithm, projection contraction algorithm and GPBB algorithm, respectively. The simulation results indicate that when a small amount of projections are acquired, the new algorithm has effectively suppressed strip artifacts and the reconstructed images show clear edges. The algorithm can greatly improved the qualify of images reconstructed from few projection data.

Blink reflex excitability in patients with fibromyalgia syndrome

Blink reflex excitability in patients with fibromyalgia syndrome