Attorney General Eric Schmitt, a Republican running for the U.S. The mandates were imposed last month as coronavirus cases and COVID-19 hospitalizations worsened because of low vaccination rates and the Delta variant. His current research interest includes application of fractional calculus and fractional partial differential equation to signal analysis, signal processing, image processing, circuits and systems, and machine intelligence.Missouri's attorney general says minors can ignore mandates issued in Kansas City and Jackson County that require mask wearing in indoor public settings, the Associated Press reported. He held several research projects, such as with the National Key Research and Development Program Foundation of China, National Nature Science Foundation of China, and Returned Overseas Chinese Scholars Project of the Education Ministry of China, and holds 13 China Inventive Patents as the first or single inventor.
He has authored or co-authored as a lead author approximately 20 papers indexed by SCI in journals such as the International Journal of Neural Systems, the IEEE Transactions on Image Processing, the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Circuits and Systems-I: Regular Papers, the IEEE Internet of Things Journal, the IEEE Intelligent Transportation Systems Magazine, IEEE ACCESS, Mathematic Methods in Applied Sciences, and Science in China Series F: Information Sciences, and Science China Information Sciences.
He is a Full Professor and a Doctoral Supervisor with the College of Computer Science, Sichuan University, the Chief Technology Officer of Chengdu PU Chip Science and Technology Company, Ltd., Chengdu, and was elected into the Thousand Talents Program of Sichuan Province and the Academic and Technical Leader of Sichuan Province.
degree from the College of Electronics and Information Engineering, Sichuan University, Chengdu, China, in 2006. These promising results will significantly improve the usability of LDCT images. Compared with state-of-the-art methods, our method obtained better results visually and numerically, especially in structural details preservation. The FTV loss can retain essential structural details while suppressing noise, generating high-quality CT images ready for interpretation by radiologists. Thirdly, extensive experimental analysis was used to evaluate the capacity of this method in suppressing noise and preserving detailed information.
Secondly, skip connections were added to optimize the network. Firstly, this paper introduced the FTV loss function for structural details enhancement.
To this end, this paper proposes a new approach for LDCT image denoising using Convolutional Neural Network (CNN) with Fractional-order Total Variation (FTV) loss, as well as residual learning.
As increasing the dose of radiation is harmful to the patient, how to trade off between reducing the radiation dose and improving the quality of the CT image has become a challenging problem. In this work, we propose a Fractional-order Residual Convolutional Neural Network (FRCNN) for Low-Dose CT (LDCT) denoising.