Ms. Jiaying Liu Profile

Jiaying Liu

at Institute of Remote Sensing and Digital Earth CAS

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Proceedings Article | 20 August 2020 Paper

Research on high-resolution quantum medical image technology for COVID-19

Siwen Bi, Hao Chen, Jiaying Liu

Proceedings Volume 11502, 1150203 (2020) https://doi.org/10.1117/12.2568467

KEYWORDS: Image processing, Image segmentation, Image processing algorithms and systems, Image enhancement, Quantum physics, Quantum communications, Remote sensing

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Considering the disadvantages of low definition, low image contrast and brightness, inconspicuous details, which make image analysis more complicate, we developed quantum medical image processing system software. It includes quantum image enhancement module, quantum image denoising module and quantum image segmentation module, which respectively study the quantum enhancement algorithm, quantum denoising algorithm and quantum segmentation algorithm. Quantum denoising algorithm firstly add noise to the image and then carries out a logarithmic transformation and a double density dual-tree complex wavelet transform on the noise-added image, later a denoising of the wavelet coefficients based on Bayesian theory, and the Maxaposterior (MAP) is performed to estimate the variance of the double-tree complex wavelet. Finally the denoised image is obtained from the inverse transform of the dual-tree complex wavelet. Quantum enhancement algorithm improves image quality through quantum inspired and unsharp masking. First, a quantum enhancement operator based on quantum superposition state theory was constructed to enhance image contrast ratio, then the processed image quality was improved by unsharp masking. Quantum segmentation algorithm searches automatically the optimal threshold through improved quantum genetic algorithm, which reduces the complexity of program and improves the information entropy compared with traditional methods. More image information can be retained and more ideal segmentation effect is realized through the algorithm. Experiment show that the peak signal-to-noise ratio (PSNR) for the quantum algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods, image information entropy and clarity index are significantly improved.

Proceedings Article | 9 September 2019 Paper

Processing algorithms for quantum remote sensing image data

Siwen Bi, Hao Chen, Yuxian Ke, Siwei Rao, Jiaying Liu

Proceedings Volume 11128, 111281A (2019) https://doi.org/10.1117/12.2528307

KEYWORDS: Image segmentation, Image processing, Image enhancement, Image processing algorithms and systems, Remote sensing, Quantum physics, Denoising, Quantum information, Image quality, Image filtering

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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.

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