Opportunities and Challenges of Intelligent Quantum Image Processing with Deep Learning

DOI

10.2991/978-94-6239-799-6_13How to use a DOI?

Keywords

Quantum Image Processing; Flexible Representation of Quantum Images (FRQI); Deep Learning; Convolutional Neural Network (CNN); Image Denoising; Hybrid Quantum–Classical Framework

Abstract

Quantum image processing (QIP) is still a promising area as the noise of qubit, decoherence and limitations of encoding make it difficult for the current quantum hardware. This paper presents a combined Intelligent Quantum Image Denoising Framework, which uses FRQI and deep learning-based restoration, to enhance the quality and trustworthiness of images in quantum-classical hybrid areas. The short overview of related studies points out the classical filters and shallow neural models denoising methods, which are not able to generalize to quantum noise. To fill this void, a Convolutional Neural Network (CNN) is trained on the FRQI-encoded images simulated with Gaussian, salt and-pepper, and depolarizing noise. The approach uses confidence-based thresholding for restoration of adaptive pixels and measures the results with PSNR, SSIM, and MSE metrics. The experimental outcomes exhibit a considerable rise in the signal-to-noise ratio and the integrity of the structure when compared to the standard denoising techniques. The research indicates that the suggested combination of different methods has the capability to get rid of quantum noise yet at the same time allow the main image features to be seen. The steps for the next research will be applying the model to actual quantum simulators like Qiskit, creating better qubit usage, and widening the application of the framework to high-resolution images for quantum vision in real-world scenarios.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Raavi Hemalatha
AU  - Jithendra Venkata Sai Bonam
PY  - 2026
DA  - 2026/09/30
TI  - Opportunities and Challenges of Intelligent Quantum Image Processing with Deep Learning
BT  - Proceedings of the International Conference on Emerging Trends and Technologies in Applications of Computer Science (ICETTACS 2026)
PB  - Atlantis Press
SP  - 174
EP  - 185
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-799-6_13
DO  - 10.2991/978-94-6239-799-6_13
ID  - Hemalatha2026
ER  -