Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach with Enhanced Error Resilience and Biological Constraint Optimization

TL;DR

This paper presents a novel deep joint source-channel coding scheme for DNA image storage that enhances error resilience and biological constraint compliance using deep learning and integrated PCR processes.

Contribution

It introduces DJSCC-DNA, a deep learning-based DNA storage method that incorporates PCR and biological constraints into the encoding and decoding process.

Findings

Outperforms traditional methods in PSNR and SSIM metrics.

Ensures positive constraints on homopolymer run-length and GC content.

Demonstrates improved data recovery accuracy in DNA storage simulations.

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.