Wu, Wenfeng and Xiang, Luping and Liu, Qiang and Yang, Kun (2023) Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 9 (4). pp. 461-471. DOI https://doi.org/10.1109/tmbmc.2023.3331579
Wu, Wenfeng and Xiang, Luping and Liu, Qiang and Yang, Kun (2023) Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 9 (4). pp. 461-471. DOI https://doi.org/10.1109/tmbmc.2023.3331579
Wu, Wenfeng and Xiang, Luping and Liu, Qiang and Yang, Kun (2023) Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 9 (4). pp. 461-471. DOI https://doi.org/10.1109/tmbmc.2023.3331579
Abstract
In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content.
Item Type: | Article |
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Uncontrolled Keywords: | DNA storage; deep learning; joint source-channel coding; biological constraints |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 09 Jan 2024 17:07 |
Last Modified: | 30 Oct 2024 21:27 |
URI: | http://repository.essex.ac.uk/id/eprint/37531 |
Available files
Filename: 2311.01122.pdf