Robust DNA Image Storage Decoding with Residual CNN

DNA storage is a promising data storage method with high density, durability, and easy maintenance, ideal for data archiving. However, wide-scale adoption is hindered by challenges like high synthesis costs, data loss, and I/O complexities. Addressing robustness is a primary concern in advancing DNA storage. Traditional strategies for robustness rely on increasing redundancy, replicas, and error-correction codes (ECC) for each DNA sequence strand. Given the unpredictable errors in DNA storage and the associated costs of absolute accuracy, we’ve embraced an error-tolerance approach. This paper introduces an innovative method utilizing the residual Convolutional Neural Network (CNN) during image decoding in DNA storage to combat noise and enhance robustness. We simulated compressed images in DNA sequences and restored them using our network, achieving commendable peak signal-to-noise ratios (PSNR) even with lower-quality images. Our method offers a balance between redundancy and image quality in DNA storage.