Advancing Efficient Error Reduction in DNA Data Storage Systems with Deep Learning‐Based Denoising Models

DNA data storage systems have made significant strides toward addressing the limitations of traditional storage media. Research in this field emphasizes innovative approaches that leverage the biochemical properties of DNA for high‐density and efficient data storage with minimal errors. This study explores the enhancement of error reduction in DNA‐based data storage systems using deep learning techniques, specifically denoising convolutional neural networks (DnCNN). This approach integrates image denoising using DnCNN models to improve the quality of images encoded into DNA sequences that are prone to errors during synthesis and sequencing. Two DNA base sequence encoding models, R ∞ ‐P8 and R 1 ‐P8, are used to assess their impact on decoded image quality. Extensive experiments reveal that the DnCNN models significantly enhance image quality, as indicated by the peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. This study demonstrates the ability of DnCNN models to correct up to 96% of substitution errors, marking a substantial improvement in data fidelity. These findings highlight the potential of combining machine learning with DNA data storage technologies to achieve higher accuracy and reliability, thereby paving the way for more robust DNA‐based storage solutions.