Abstract :

In Order To Reduce Storage And Transmission Costs Without Sacrificing Diagnostic Quality, Highresolution Medical Images Such As CT, MRI, And Chest X-rays Require Efficient Compression. By Extracting Quantum Features, Quantum-Enhanced Artificial Neural Networks (QANN) Improve Compression Efficiency; Nevertheless, Higher Compression Ratios May Result In Worse Image Quality. To Get Over This Limitation, This Study Enhances The QANN-based Medical Image Compression Framework With A Super Resolution Enhancement Module. The Hybrid Method Employs Super Resolution Processing To Recover Tiny Structural Characteristics, Reduce Noise, And Improve Visual Clarity After Using QANN For Quantum-assisted Compression And Reconstruction. Real-time Augmentation Is Possible Without Increasing System Complexity Because To The Computationally Effective Super Resolution Module. Users May Upload Medical Photographs, Compress, Reconstruct, And Enhance Super Resolution Using An Interactive Flask Web Interface. According To Research, The Extended Framework Improves Reconstructed Image Quality While Maintaining High Compression Efficiency, Which Makes It Appropriate For Real-time, Bandwidth-constrained Medical Imaging Applications Like Telemedicine And Remote Diagnostics.

Published:

07-1-2022

Issue:

Vol. 22 No. 1 (2022)

Page Nos:

36 - 44

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite