scholarly journals A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 644 ◽  
Author(s):  
Shang Zhang ◽  
Yuhan Dong ◽  
Hongyan Fu ◽  
Shao-Lun Huang ◽  
Lin Zhang
Keyword(s):  
Dictionary Learning ◽  
Reconstruction Algorithm ◽  
Sparse Optimization ◽  
Spectral Reconstruction

A spectral reconstruction algorithm for two-plane Compton cameras

2020 ◽  
Vol 65 (2) ◽  
pp. 025011
Author(s):  
Enrique Muñoz ◽  
Luis Barrientos ◽  
José Bernabéu ◽  
Marina Borja-Lloret ◽  
Gabriela Llosá ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Reconstruction

scholarly journals Dense light field reconstruction algorithm based on dictionary learning

2020 ◽  
Vol 69 (6) ◽  
pp. 064201
Author(s):  
Zheng-De Xia ◽  
Na Song ◽  
Bin Liu ◽  
Jin-Xiao Pan ◽  
Wen-Min Yan ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Light Field ◽  
Reconstruction Algorithm ◽  
Field Reconstruction

Incomplete angle reconstruction algorithm with the sparse optimization and the image optimal criterions

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091697
Author(s):  
Shengxi Jiao ◽  
Lu Wen ◽  
Haitao Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Optimization Criterion ◽  
Sparse Optimization ◽  
Reconstruction Technique ◽  
Split Bregman ◽  
Image Optimization ◽  
Alternating Direction ◽  
Augmented Lagrange Method

To solve the problem of artifact and image degradation caused by incomplete angle projection, this article presents an incomplete angle reconstruction algorithm based on sparse optimization and image optimization criterion (SO-IOC). Firstly, the joint objective function model is established based on the projection sparsity and the natural features of images. Secondly, by means of the idea of alternating direction method of multipliers, the augmented Lagrange method is used to decompose the reconstruction model into simple subproblems and the modified genetic algorithm is used for solving those subproblems. Finally, a multiobjective optimization operation is carried out to coordinate and select the candidate solutions to improve the quality of the reconstructed images. The algebraic reconstruction technique algorithm and the Split Bregman algorithm are compared with the SO-IOC algorithm. In the compared process, the mean relative error and the peak signal-to-noise ratio are used. The experimental results show the SO-IOC algorithm is best among the above three algorithms.

scholarly journals Application of FT-Raman and FTIR measurements using a novel spectral reconstruction algorithm

2003 ◽  
Vol 34 (10) ◽  
pp. 795-805 ◽  
Author(s):  
Su Ying Sin ◽  
Effendi Widjaja ◽  
Liya E. Yu ◽  
Marc Garland
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Reconstruction ◽  
Ft Raman

scholarly journals Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Li ◽  
Yanbo Zhang ◽  
Shuwei Mao ◽  
Jiehua Zhu ◽  
Yangbo Ye
Keyword(s):  
Color Image ◽  
Reconstruction Algorithm ◽  
Computational Time ◽  
Spectral Information ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Clinical Images ◽  
X Ray ◽  
Spiral Scanning ◽  
Spectral Reconstruction

Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.

scholarly journals Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Liang ◽  
Shuxing Liu
Keyword(s):  
Dictionary Learning ◽  
Dirichlet Process ◽  
Prior Probability ◽  
Learning Algorithm ◽  
Probability Model ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Local Similarity ◽  
Probability Modeling ◽  
Comparison Algorithms

In order to make full use of nonlocal and local similarity and improve the efficiency and adaptability of the NPB-DL algorithm, this paper proposes a signal reconstruction algorithm based on dictionary learning algorithm combined with structure similarity clustering. Nonparametric Bayesian for Dirichlet process is firstly introduced into the prior probability modeling of clustering labels, and then, Dirichlet prior distribution is applied to the prior probability of cluster labels so as to ensure the analyticity and conjugation of the probability model. Experimental results show that the proposed algorithm is not only superior to other comparison algorithms in numerical evaluation indicators but also closer to the original image in terms of visual effects.

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