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240306s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3371351
|2 doi
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|a eng
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| 100 |
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|a Gan, Hongping
|e verfasserin
|4 aut
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| 245 |
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|a NesTD-Net
|b Deep NESTA-Inspired Unfolding Network With Dual-Path Deblocking Structure for Image Compressive Sensing
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|c 2024
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 18.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Deep compressive sensing (CS) has become a prevalent technique for image acquisition and reconstruction. However, existing deep learning (DL)-based CS methods often encounter challenges such as block artifacts and information loss during iterative reconstruction, particularly at low sampling rates, resulting in a reduction of reconstructed details. To address these issues, we propose NesTD-Net, an unfolding-based architecture inspired by the NESTA algorithm, designed for image CS. NesTD-Net integrates DL modules into NESTA iterations, forming a deep network that continuously iterates to minimize the l1 -norm CS problem, ensuring high-quality image CS. Utilizing a learned sampling matrix for measurements and an initialization module for initial estimate, NesTD-Net then introduces Iteration Sub-Modules derived from the NESTA algorithm (i.e., Yk , Zk , and Xk ) during reconstruction stages to iteratively solve the l1 -norm CS reconstruction. Additionally, NesTD-Net incorporates a Dual-Path Deblocking Structure (DPDS) to facilitate feature information flow and mitigate block artifacts, enhancing image detail reconstruction. Furthermore, DPDS exhibits remarkable versatility and demonstrates seamless integration with other unfolding-based methods, offering the potential to enhance their performance in image reconstruction. Experimental results demonstrate that our proposed NesTD-Net achieves better performance compared to other state-of-the-art methods in terms of image quality metrics such as SSIM and PSNR, as well as visual perception on several public benchmark datasets
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|a Journal Article
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1 |
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|a Guo, Zhen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Feng
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 07., Seite 1923-1937
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|x 1941-0042
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|g volume:33
|g year:2024
|g day:07
|g pages:1923-1937
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|u http://dx.doi.org/10.1109/TIP.2024.3371351
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