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In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges asan intriguing approach, garnering substantial academic interest andinvestigation. This paper introduces a novel deep joint source-channel coding(DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigmdistinguishes itself from conventional DNA storage techniques through three keymodifications: 1) it employs advanced deep learning methodologies, employingconvolutional neural networks for DNA encoding and decoding processes; 2) itseamlessly integrates DNA polymerase chain reaction (PCR) amplification intothe network architecture, thereby augmenting data recovery precision; and 3) itrestructures the loss function by targeting biological constraints foroptimization. The performance of the proposed model is demonstrated vianumerical results from specific channel testing, suggesting that it surpassesconventional deep learning methodologies in terms of peak signal-to-noise ratio(PSNR) and structural similarity index (SSIM). Additionally, the modeleffectively ensures positive constraints on both homopolymer run-length and GCcontent.

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