Quantum Vision–Guided Xception for Robust Visual Feature Learning Under Image Distortions
DOI:
https://doi.org/10.63665/IJAICE.0203.02Keywords:
Quantum Vision (QV), Heavy QV-Xception, Object Recognition, Deep Learning, Xception, Wave-Function Representation, Quantum-Inspired Computing, Depthwise Separable Convolution, Convolutional Neural Networks, Feature ExtractionAbstract
This work proposes a novel Heavy QV-Xception model for advanced object recognition by integrating the wave-centric principles of Quantum Vision (QV) theory with the efficient architecture of Xception. Inspired by the particle-wave duality of quantum physics, QV theory interprets visual objects as dynamic information waves rather than static pixel-based images. The proposed framework incorporates a robust QV transformation block at the frontend to convert conventional spatial images into high-dimensional wave-function representations. This transformation enables the model to capture complex structural patterns, non-local statistical relationships, and dynamic contextual information that may not be effectively represented through conventional localized pixel processing.
The transformed wave-based features are subsequently processed through the Xception backbone, which employs depthwise separable convolutions to efficiently separate spatial feature extraction from cross-channel feature correlation. By combining quantum-inspired information representation with efficient deep convolutional learning, the Heavy QV-Xception model provides enhanced feature extraction and improved object recognition capability. Experimental evaluation on multiple benchmark datasets demonstrates that the proposed model consistently outperforms standard Xception and other conventional convolutional neural network models. The results highlight the potential of integrating Quantum Vision theory with advanced deep learning architectures to achieve more accurate and robust object recognition.
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