RETRACTED ARTICLE: Quantum-inspired adaptive loss detection and real-time image restoration for live optical quantum image transmission

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.