Authors
Siyuan Yao, Xin Zhang, Mengzhe Shen, Yang Liu, Yu Jiao, Xindong Chen
Published in
Optics letters. Volume 51. Issue 15. Pages 4276-4279. Aug 01, 2026.
Abstract
High-throughput sequencing requires breaking the resolution limit to support higher-density DNA Nanoball (DNB) arrays, thereby reducing cost and increasing sequencing throughput. Traditional multi-frame super-resolution methods, such as structured illumination microscopy (SIM), sacrifice imaging speed, leading to reduced throughput. This paper proposes a non-deep-learning single-frame super-resolution algorithm based on sequencing scene priors, named SFS-seq. It requires no training data or GPU, directly decoupling DNB brightness from a single wide-field image with O(N) per iteration. Experiments show that SFS-seq reconstructs brightness highly consistent with SIM, achieving a correlation coefficient as high as 0.9852, and its base calling accuracy significantly outperforms direct use of wide-field images. SFS-seq combines speed, accuracy, and low cost, providing a single-frame solution for high-throughput sequencing.
PMID:
42537167
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.
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