Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network

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.