Quantum image processing represents a pivotal field in quantum remote sensing1-3. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications4-5, while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging6-8. Through systematic research and experiments9-11, we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields12-15. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.
Si-wen et al. (Wed,) studied this question.