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Real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis


Image is the basis of human vision and the most commonly used information carrier in social activities. In the field of medicine, medical imaging obtains the image of a certain part of tissue in the body. There are many ways of medical imaging and acquisition systems, such as X-ray, magnetic resonance imaging, computed tomography, endoscopy, ultrasound and positron emission tomography and microscopic imaging. Medical image provides a more intuitive visual means, which provides a reliable basis for disease diagnosis, clinical treatment and teaching research. In real life, many different kinds of noise will affect the image collection process, these factors will make the quality of the image to a large extent. In the process of image denoising, white noise is a very common kind of noise. Even a high-resolution image will have some noise, but some simple denoising algorithms can be used for these noises. However, in this era of high resolution, the real-time performance of denoising is not good, and the real-time denoising is still the subject of many studies. In order to enhance the visual sense of the image by filtering out the noise in the image, we need to filter the two-dimensional image, and then make the operation of the image easier. In this paper, we study the real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis. The experimental results show that this method can effectively deal with the noise points in the medical image, and the result graph is better.

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The research was funded by Shanxi soft science research project (general project). Project No: 2019041003–1. Project Name: Study on the geographical spatial differentiation, influence mechanism and optimization path of regional innovation ability in Shanxi Province.


Shanxi soft science research project (general project). Project No: 2019041003–1. Project Name: Study on the geographical spatial differentiation, influence mechanism and optimization path of regional innovation ability in Shanxi Province.

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GG contributed to designed the model, collected dataset, performed the analysis, validated the results, written and reviewed the manuscript.

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Correspondence to Guangxing Guo.

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Communicated by V. Suma.

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Guo, G. Real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis. Soft Comput 27, 4263–4278 (2023).

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  • Wavelet analysis
  • Multiscale model
  • Medical image
  • Image denoising
  • Mobile edge computing
  • Autonomous unmanned analysis