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<p>Securing medical images during transmission is a challenge in this cyber world due to the patient data privacies’ and the limitations of conventional encryption and compression techniques, which often compromise either security or efficiency. To address this issue, a dual-framework approach that integrates Generative Adversarial Networks (GANs) for encryption with an Enhanced Super Resolution Network (ESRNet) for reconstruction has been proposed in this work. The medical images are divided into 3 ⋅ 3 segments, encrypted using a random sequence generator based on Henon and Tent maps, and downsampled to one-fourth of their original resolution for efficient transmission, while ESRNet at the receiver end reconstructs the high-resolution image. The experimental results demonstrate the robustness of this framework, achieving significant performance, along with a minimum of 26.7% improvement in processing speed to a maximum of 98.2% improvement when compared to existing methods. Also, the proposed methodology significantly enhances data security, reduces storage and transmission requirements, offers a practical solution for medical image handling in healthcare systems.</p>

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A Dual-Framework Approach for Medical Image Encryption and Reconstruction using GANs and Super-Resolution Networks

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Rajasekaran Gopalsamy,
  • Yogarajan Gunasekaran,
  • Rakshika Saravanan,
  • Subasree Pothilingam

摘要

Securing medical images during transmission is a challenge in this cyber world due to the patient data privacies’ and the limitations of conventional encryption and compression techniques, which often compromise either security or efficiency. To address this issue, a dual-framework approach that integrates Generative Adversarial Networks (GANs) for encryption with an Enhanced Super Resolution Network (ESRNet) for reconstruction has been proposed in this work. The medical images are divided into 3 ⋅ 3 segments, encrypted using a random sequence generator based on Henon and Tent maps, and downsampled to one-fourth of their original resolution for efficient transmission, while ESRNet at the receiver end reconstructs the high-resolution image. The experimental results demonstrate the robustness of this framework, achieving significant performance, along with a minimum of 26.7% improvement in processing speed to a maximum of 98.2% improvement when compared to existing methods. Also, the proposed methodology significantly enhances data security, reduces storage and transmission requirements, offers a practical solution for medical image handling in healthcare systems.