Publications

This paper presents a novel approach to image restoration by integrating quantum computing principles with contrastive adversarial networks. Our quantum-enhanced method demonstrates improved performance in noise reduction and feature preservation across various image degradation scenarios. The integration of quantum circuits enables more efficient processing of high-dimensional image data while maintaining the adversarial training benefits for realistic image reconstruction.

We present a comprehensive MATLAB-based simulator for free-floating spacecraft manipulators using Lagrangian formulation. The simulator features configurable links, inertias, joints, DH parameters, and end-effectors. The system has been independently validated against MuJoCo, demonstrating accurate zero-gravity multi-body dynamics. This tool enables researchers to study spacecraft manipulator dynamics for space missions including debris capture and satellite servicing operations.

This work introduces quantum-based methods for image compression using scalable amplitude embedding. We propose four compression strategies and demonstrate that key image patterns are preserved at up to 75% compression rates, enabling effective subsequent processing. The quantum approach achieves exponential compression scaling compared to classical methods while maintaining image quality for computer vision applications.

We implement a quantum-circuit-based image fusion approach to enhance SAR image quality by developing eight quantum processing techniques to fuse SAR and optical data. This data-centric approach improves CNN land-use classification accuracy from 82.64% to 95.36%, demonstrating the effectiveness of quantum-enhanced data preprocessing for remote sensing applications.

We developed a remote sensing dataset comprising 3,800 SAR and optical images from Sentinel-1 and Sentinel-2 missions, acquired from Google Earth Engine and preprocessed using quantum algorithms for deep learning and scene classification. This dataset enables researchers to explore quantum-enhanced remote sensing applications.

This thesis explores quantum-assisted techniques for fusing SAR and optical imagery to improve scene classification. The work demonstrates how quantum computing principles can enhance traditional image processing workflows, particularly for remote sensing applications in space technology.