The algorithm of regionally compensating nonlinear response of photo detector to improve the quality of image reconstruction for compressed sensing

2013 ◽  
Author(s):  
Jiayan Zhuang ◽  
Qian Chen ◽  
Zhuang Miao ◽  
Weiji He ◽  
Weiyi Feng
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Nonlinear Response

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

scholarly journals Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 182
Author(s):  
Rongfang Wang ◽  
Yali Qin ◽  
Zhenbiao Wang ◽  
Huan Zheng
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Structural Similarity ◽  
Block Matching ◽  
Reconstructed Image ◽  
Local Similarity ◽  
Iterative Thresholding ◽  
Regularization Term

Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of the group coefficients and reduces the complexity of the model. The group sparse residual regularization term introduces the prior information of the image to improve the quality of the reconstructed image. The alternating direction multiplier method and iterative thresholding algorithm are applied to solve the optimization problem. Simulation experiments confirm that the optimized GSR-JR model is superior to other advanced image reconstruction models in reconstructed image quality and visual effects. When the sensing rate is 0.1, compared to the group sparse residual constraint with a nonlocal prior (GSRC-NLR) model, the gain of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) is up to 4.86 dB and 0.1189, respectively.

scholarly journals Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4202
Author(s):  
Ruili Nan ◽  
Guiling Sun ◽  
Zhihong Wang ◽  
Xiangnan Ren
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Linear Mapping ◽  
Growth Monitoring ◽  
Reconstruction Method ◽  
Residual Network ◽  
Multi Scale ◽  
Reconstruction Methods ◽  
Image Reconstruction Method

In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

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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