Abstract
Maintaining image fidelity during transmission is challenging for live optical quantum image transmission. This paper introduces a novel "Quantum-Inspired Adaptive Loss Detection and Real-time Image Restoration" approach. The method incorporates adaptive loss detection and real-time restoration techniques, drawing inspiration from quantum principles to model the optical quantum environment. The core innovation is a near-to-far continuous approach adapted to the quantum environment's dynamics, enhancing image clarity and quality. A Network-in-Network architecture with MLPConv layers is proposed for the system model to estimate the transmission map for image de-hazing using the Reinforcement Learning system (ID-RL). A depth-aware dehazing reinforcement learning framework tackles image regions separately. Experiments demonstrate superior over prior SSIM and PSNR arts, even with minimal training data. Efficiency for real-time usage is shown, with potential for autonomous surveillance applications in smart cities. This quantum-inspired adaptive technique is a promising advancement for live optical quantum image transmission fidelity.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Inquiries about data availability should be directed to the authors.
References
Dong, H.; Pan, J.; Xiang, L.; Hu, Z.; Zhang, X.; Wang, F.; Yang, M.H. Multi-Scale boosted de-hazing network with dense feature fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 2157–2167
Fan, G., Hua, Z., Li, J.: Multi-scale depth information fusion network for image de-hazing. Appl. Intell. 51, 7262–7280 (2021)
Guo, T.; Monga, V. 2020 Reinforced depth-aware deep learning for single image de-hazing. In Proceedings of the ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4–8 May pp. 8891–8895.
He, Z, and Patel, VM densely connected pyramid de-hazing network, Proc.IEEE Conference on Computer Vision & Pattern Recognition, 2018.
Kachhoria, R., Jaiswal, S., Khairnar, S., Rajeswari, K., Pede, S., Kharat, R., Galande, S., Khadse, C.: Lie group deep learning technique to identify the precision errors by map geometry functions in smart manufacturing. Int. J. Adv. Manuf. Technol. (2023). https://doi.org/10.1007/s00170-023-10834-2
Leonid, T.T., Hemamalini, N.T., Krithika: Fruit quality detection and classification using Computer Vision Techniques. In: 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1–6, doi: https://doi.org/10.1109/ICONSTEM56934.2023.10142514.
Priyalakshmi, B., Verma, P.: Navigational tool for blind people. AIP Conf. Proc. 2460(1), 020015 (2022). https://doi.org/10.1063/5.0095640
Ratna, K., Sumanth, C.D., Anshika Ram, B., Yadav, S.K., Hemalatha, G.: Analytical investigation of MR damper for vibration control: a review. J. Appl. Eng. Sci. 11(1), 49–52 (2021)
Sasikala, P.; Aravinth, TS, Dharani, KG, Mohanarathinam, A and Jayachitra S, "IoT based Smart Intelligent Vehicle Systems," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 1094–1097.
Sheng, J., Lv, G., Du, G., Wang, Z., Feng, Q.: Multi-Scale residual attention network for single image de-hazing. Digit. Signal Process. 121, 103327 (2022)
Wang, J., Li, C., Xu, S.: An ensemble multi-scale residual attention network (EMRA-net) for image De-hazing. Multimedia. Tools Appl. 80, 29299–29319 (2021)
Zhang, J., Tao, D.: FAMED-Net: A fast and accurate multi-scale end-to-end de-hazing network. IEEE Trans. Image Process. 29, 72–84 (2019)
Zhao, D., Xu, L., Ma, L., Li, J., Yan, Y.: Pyramid global context network for image de-hazing. IEEE Trans. Circuits Syst. Video Technol. 31, 3037–3050 (2020)
Funding
Not Applicable.
Author information
Authors and Affiliations
Contributions
T.P.P and V.L.N conducted the primary research, data collection, and experimentation. R. R and K. S provided critical guidance, oversight, and expertise throughout the research process. J.M played a pivotal role in data analysis and algorithm development. Y.V. M contributed to the theoretical framework and the interpretation of results. All authors collectively participated in manuscript preparation, editing, and finalizing the research paper for submission.
Corresponding author
Ethics declarations
Conflict of interest
At this moment, I declare that I have no conflicts of interest with other works. Any agency does not fund the study. The authors declare that there is no conflict of interest with other works regarding the publication of this paper.
Human and animal rights
This article contains no studies with human participants or animals performed by authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Priyanka, T.P., Reji, R., Narla, V.L. et al. Quantum-inspired adaptive loss detection and real-time image restoration for live optical quantum image transmission. Opt Quant Electron 56, 411 (2024). https://doi.org/10.1007/s11082-023-05859-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-023-05859-6