Novel algorithm to compensate nonlinear response of photo detector to improve quality of image reconstruction for compressed sensing

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.

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

Electronics ◽

10.3390/electronics11020182 ◽

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.

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A Novel Algorithm to Improve Quality of Service of Cell Edge Users in LTE

International Conference on Advanced Computing Networking and Informatics - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-13-2673-8_26 ◽

2018 ◽

pp. 237-245

Author(s):

Himani Lodwal ◽

Anjulata Yadav ◽

Manish Panchal

Keyword(s):

Quality Of Service ◽

Improve Quality ◽

Cell Edge ◽

Novel Algorithm

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

Sensors ◽

10.3390/s20154202 ◽

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.

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