Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network |
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| Affiliation: | 1. Department of Electronics and Communication Engineering, National Institute of Technology, Thiruchirappalli, India;2. University of Saskatchewan, Canada;1. Department of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, South Korea;2. Engineering Research Center on Cloud Computing & Internet of Things and E-commerce Intelligence of Fujian Universities Quanzhou Normal University, No. 398, Donghai Street, Fengze District, Quanzhou 362000, China;3. School of Economics and Management, Xinyu University, No. 2666, Yangguang Street, Xinyu 338004, China;1. School of Architecture, South China University of Technology, Guangzhou 510641, China;2. Foreign Language Teaching Department, Guang Zhou Vocational School of Finance and Economics, Guang Zhou 510080, China;3. School of Financial Mathematics and Statistics, GuangDong University of Finance, Guangzhou 510521, China;1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China;2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China;3. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;4. Wuhan University of Technology, Wuhan 430070, China;1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410014, China;2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha 410014, China;3. School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK |
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| Abstract: | In clinical analysis and diagnosis, high resolution (HR) computed tomography (CT) images are required for proper treatment of a patient. Developing HR medical images by X-ray CT devices require extended radiation exposure with large radiative dosages, putting the patient at potential risk of inducing cancer. So, radiation exposure should be reduced. However, photon starvation and beam hardening in low-dose X-rays will cause severe artifacts. Thus, an accurate reconstruction of low-dose X-ray CT images is required. To this end, we propose a wavelet based multi-channel and multi-scale cross connected residual-in-dense grouped convolutional neural network (WCRDGCNN) for accurate super resolution (SR) of medical images. The adopted filter groups reduce the connection weights, thereby reducing the computational complexity. Gradient vanishing problem is tackled by using residual and dense skip connections. The extensive experimentation results on benchmark datasets show that our method outperforms the state-of-the-art SR methods. |
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| Keywords: | Grouped convolution Low-dose X-ray CT Residual-in-dense Super resolution Wavelet sub-bands |
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