Ultra-Low-Light Imaging Enhancement Using Quantum-Inspired Neural Networks
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I2P113Keywords:
Ultra-Low-Light Imaging, Neural Network Inspired By Quantum, Photon-Starved Imaging, Noise Modeling, Probabilistic Encoding, Deep Learning, Hybrid CNN-QINN, Biomedical Imaging, Astronomical Imaging Pipelines, Secure Imaging PipelinesAbstract
Extremely low-light imaging is essential to the very diverse applications of biomedical microscopy, astronomical observation, surveillance and remote sensing, where photon-limited conditions severely impair the quality of an image. Traditional ways of enhancement are not able to perform well in these extreme lighting conditions and tend to increase noise and blur structural features. Recent developments on quantum-inspired neural networks (QINNs) offer a good alternative option through probabilistic encoding of amplitude, energy-based optimization, and uncertainty-aware feature refinement, but can be implemented on classical hardware. In this review, the authors provide a detailed overview of QINNs in ultra-low-light imaging enhancement, including the basic concepts, sensor technologies, the Deep Learning style, quantum-inspired solutions, hybrid frameworks, and areas of application. Also, the review focuses on security, trust, and policy provisions applicable to deployment in sensitive domains, such as biomedical, defense, and cloud-based imaging systems. The most common challenges, including model interpretability, scalability in real-time, and non-standard benchmarks are identified that can act as a roadmap of the future studies. This paper has presented an impartial view of the state-of-the-art of quantum-inspired low-light image enhancement by synthesizing the progress made in this area.
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