Since Professor Siwen Bi proposed quantum remote sensing (QRS) in early 2001, the first QRS imaging prototype was developed after many stages of researches. Based on the results, our group has also undertaken in-depth theoretical and algorithmic experiments on QRS image processing. It’s both a quantum system simulation algorithm, preparing for the future quantum physical devices and calculation technology, and an expansion of quantum theories to RS image processing fields. It combines quantum mechanics theory and RS image processing technology, which introduces a new research direction for RS image processing technology. Now our researches achievements include a quantum denoising algorithm theory and simulation, a quantum enhancement algorithm theory and simulation, and a quantum segmentation algorithm theory research and simulation. A RS denoising algorithm based on the quantum-inspired concept is proposed for image denoising. Key benefits of the algorithm, which include improvements in transmission and accuracy, are demonstrated experimentally. Experiments showed that the peak signal to noise ratio (PSNR) for the proposed algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods. Given the low contrast ratio and brightness as well as insufficient detail for some RS images, a quantum algorithm based on the combination of a quantum inspired and unsharp masking to enhance and segment the RS image data was proposed. Results showed that the contrast ratio and brightness of images processed by the quantum algorithm improved, the image entropy and peak signal to noise ratio is higher.
Since Professor Siwen Bi proposed quantum remote sensing (QRS) in early 2001, the first QRS imaging prototype was developed after many stages of researches. Based on the results, our group has also undertaken in-depth theoretical and algorithmic experiments on QRS image processing. It’s both a quantum system simulation algorithm, preparing for the future quantum physical devices and calculation technology, and an expansion of quantum theories to RS image processing fields. It combines quantum mechanics theory and RS image processing technology, which introduces a new research direction for RS image processing technology. Now our researches achievements include a quantum denoising algorithm theory and simulation, a quantum enhancement algorithm theory and simulation, and a quantum segmentation algorithm theory research and simulation. A RS denoising algorithm based on the quantum-inspired concept is proposed for image denoising. Key benefits of the algorithm, which include improvements in transmission and accuracy, are demonstrated experimentally. Experiments showed that the peak signal to noise ratio (PSNR) for the proposed algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods. Given the low contrast ratio and brightness as well as insufficient detail for some RS images, a quantum algorithm based on the combination of a quantum inspired and unsharp masking to enhance and segment the RS image data was proposed. Results showed that the contrast ratio and brightness of images processed by the quantum algorithm improved, the image entropy and peak signal to noise ratio is higher.
Based on the development and application needs, Professor Siwen Bi has proposed quantum remote sensing (QRS) in the early 2001. Firstly, the paper introduces the background, concept and research status at home and abroad of quantum remote sensing, it also introduces the differences and advantages of quantum remote sensing compared with the classical remote sensing. Then it elaborates wave-particle duality, Superposition State, Tunneling Effect of QRS research basis and quantum entanglement, its basic theory and information mechanism, as well as the research progress of QRS imaging processing, quantum spectrum remote sensing and QRS calibration. Besides, it emphasizes the experimental process of QRS imaging and the production of principle prototype, based on which, Professor Bi proposed active imaging information transmission technology of satellite borne QRS technical solutions and conducted research on its system composition and working principle, squeezing light preparation of active imaging, injection device and quantum non-noise amplifier device, providing solutions and technical basis for realizing active imaging technology of satellite-borne QRS. The QRS imaging technology can significantly improve the signal-to-noise ratio and space resolution of QRS imaging information transmission. Finally, the paper summarizes the research conditions over the past eighteen years and plan for the future.
Remote sensing is an advanced technology to explore the space and acquire earth-space information, with its accurate, comprehensive and dynamic detection and observation ability, RS plays an key role in earth observation, national defense, aerospace and deep space exploration. The advancement of science and urgent demands in economy and other fields put forward higher requirements for RS. Firstly, some important theoretical issues need to be addressed, such as RS information theory, spectral imaging mechanism, electromagnetic radiation transmission model. Secondly, the resolution power of RS instruments and their ability to utilize information needs to be improved. Finally, more detailed and richer RS information is needed during application. Of all these requirements, higher resolution, spectrum resolution, time resolution and further applications of RS are extremely important. While quantum remote sensing (QRS) performs very well in these aspects. QRS study was began in March, 2000, the concept was proposed in early 2001, then QRS technology researches began. The paper firstly introduces its background, concept and research status, its differences and advantages. Then it elaborates QRS research theoretical basis, information mechanism, quantum spectrum imaging research progress of QRS imaging processing. Besides, it emphasizes QRS imaging experimental process, based on which, technical solutions and design of satellite borne QRS active imaging technology and quantum laser radar engineering prototype were proposed, providing technical basis for their application. QRS imaging technology can significantly improve the signal-to-noise ratio and space resolution. Finally, the paper summarizes the researches over the past nineteen years and plans for the future.
High resolution, high signal-to-noise ratio and high sensitivity have always been the goals and technical bottlenecks of detection, precision measurement and imaging. Quantum detection technology is one of the disruptive cutting-edge technologies in the world. Quantum information technology is introduced into the field of classical detection, hoping to solve the difficult technical bottleneck on the basis of classical physics. Compared with traditional detection methods, quantum detection has higher sensitivity, can receive weaker signals, requires lower transmission power, and has stronger anti-interference ability. In terms of precision measurement, the characteristics of quantum mechanics are used to make the measurement accuracy break through the classical physical limit, and then improve the measurement accuracy of the detection system. But with the development of remote sensing technology and people demand for long distance detection, we completed in the early completed close quantum detection, combined with remote sensing technology, the quantum squeezed light into the receiving system, using quantum phase-free amplification technology, make up for the low efficiency of the detector, make its imaging results close to the ideal detector. We designed a set of quantum squeezing light preparation, emission /receiving system, quantum enhancement module, quantum imaging system, balance homodyne detection system, data processing system, central control system in one of the remote quantum infrared detector, effective detection distance up to 100 meters, at the same time achieve high resolution imaging, the same environment and classical laser imaging resolution increased by 1 to2 times. In March 2000, Professor Siwen Bi conducted the exploration and research on quantum remote sensing. At the beginning of 2001, Professor Siwen Bi first put forward the concept of quantum remote sensing at home and abroad. In the past 20 years, through three stages of basic theoretical scientific experiments and key technologies, we have overcome many difficulties in theoretical research scientific experiments and key technologies ,and achieved breakthrough progress and innovative results, which have laid a key foundation for the follow-up quantum laser radar quantum remote sensing products.
Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research we propose a novel approach to address the issue of noise in QIP. By training and employing a machine learning model that identifies and corrects the noise in quantum processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum processed images from open access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research, we propose a novel approach to address the issue of noise in QIP. By training and employing a machine learning model that identifies and corrects the noise in quantum-processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum-processed images from open-access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.