内部:基于可逆神经网络的DNA图像存储与自我纠正编码
Yanfen Zheng1, Xiaokang Zhang1, Lijun Sun1
1School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China.
Computational and structural biotechnology journal
|June 23, 2025
概括
通过一种新的可逆神经网络方法 (INNSE) 改进了DNA数据存储. 这种方法提高了图像和视频的编码密度和错误校正,超过了现有的算法.
科学领域:
- 生物技术是生物技术.
- 数据存储数据存储数据存储
- 生物信息学是一种生物信息学.
背景情况:
- DNA 数据存储提供了一个高密度的替代传统基媒体.
- 当前的DNA存储方法与多式联络数据作斗争,在编码密度,时间复杂性和插入/删除的错误纠正方面存在局限性.
研究的目的:
- 开发一种先进的DNA数据存储方法,克服多式联络数据现有技术的局限性.
- 为了提高编码密度,减少时间复杂性,并增强DNA存储系统的错误校正能力.
主要方法:
- 提出了一个基于可逆神经网络的DNA图像存储自我校正编码方法 (INNSE).
- 利用网络可逆性进行图像和视频数据的上下采样,以尽量减少DNA序列要求.
- 设计了一个代码库生成器,用于将低维向量编码成受约束的DNA序列.
- 实施了一种自我纠正机制,用于在没有冗余信息的情况下纠正基层DNA序列错误.
主要成果:
- 与阴阳算法相比,INNSE实现了编码密度的3倍增加.
- 通过INNSE方法,编码时间复杂性减少了95.26%.
- 在2%的模拟错误率下,INNSE将PSNR提高了80.88%,SSIM提高了63.82%.
结论:
- INNSE在编码性能和DNA数据存储的错误纠正能力方面具有显著的优势.
- 拟议的方法有效地解决了存储多式联运数据的挑战,提供了更高效和更强大的解决方案.
- INNSE在使DNA数据存储在复杂数据类型中变得实用方面取得了重大进展.
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