Image processing in DNA

TL;DR

This paper introduces a novel DNA data storage method for images that leverages signal processing and machine learning to correct errors without redundancy, reducing costs and improving practicality.

Contribution

It is the first to apply signal processing and machine learning techniques for error correction in DNA image storage, avoiding the need for redundant oligos or rewriting.

Findings

Successful storage and retrieval of images in DNA demonstrated experimentally.

Error correction achieved through discoloration detection and inpainting techniques.

Reduces storage costs by eliminating the need for redundancy or rewriting.

Abstract

The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual oligo (fragment) symbol errors as well as missing DNA oligo errors, with rates that exceed those of modern storage systems by orders of magnitude. These errors can be corrected either through the use of a large number of redundant oligos or through cycles of writing, reading, and rewriting of information that eliminate the errors. Both approaches add to the overall storage cost and are hence undesirable. Here we propose the first method for storing quantized images in DNA that uses signal processing and machine learning techniques to deal with error and cost issues without resorting to the use of redundant oligos or rewriting. Our methods rely on decoupling the RGB channels of images, performing specialized quantization and compression on the individual color channels, and using new discoloration detection and image inpainting techniques. We demonstrate the performance of our approach experimentally on a collection of movie posters stored in DNA.