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Related Experiment Video

Updated: Jun 29, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Privacy-Preserving CNN Inference for Image Super-Resolution Cross Multiple Ciphertexts.

Peijia Zheng, Donger Mo, Yufei Zhou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a secure method for image super-resolution (SR) using homomorphic encryption. The novel approach enhances privacy for online SR services by protecting plaintext images during processing, achieving significant speed and memory improvements.

    Area of Science:

    • Computer Vision
    • Cryptography
    • Machine Learning

    Background:

    • Online image super-resolution (SR) services are popular but pose privacy risks due to plaintext image exposure.
    • Existing secure CNN inference methods are unsuitable for SR due to the increased output resolution.
    • Protecting sensitive image data during SR processing is a critical challenge.

    Purpose of the Study:

    • To develop a privacy-preserving scheme for Convolutional Neural Network (CNN)-based image super-resolution.
    • To enable secure inference for image SR applications without compromising data privacy.
    • To improve the efficiency and practicality of secure image SR.

    Main Methods:

    • A novel multiple ciphertext encapsulation method for secure CNN inference in image SR.

    Related Experiment Videos

    Last Updated: Jun 29, 2026

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

  • Design of fundamental homomorphic operations (addition, multiplication, rotation) across ciphertexts.
  • Development of a fast, secure upsampling algorithm using pre-weight block masking and cross-ciphertext rotation.
  • Implementation of an efficient batched homomorphic 2D convolution with kernel optimization.
  • Creation of a polynomial activation function tailored for SR tasks.

    • Main Results:

    • The proposed HE-friendly SR network demonstrates superior performance compared to existing secure solutions.
    • The multiple ciphertext encapsulation technique achieves at least a 2x improvement in computational efficiency.
    • Significant reduction in memory usage was observed with the new encapsulation method.
    • The secure upsampling algorithm offers substantial speedup over direct homomorphic upsampling.

    Conclusions:

    • The developed scheme effectively addresses privacy concerns in online image SR services.
    • The novel techniques provide a practical and efficient solution for secure image super-resolution.
    • This work paves the way for more secure and private AI-driven image enhancement applications.