Bone induced artifacts caused by spectral absorption of skull is intrinsic to head images in CT. Artifacts which blur the images and further temper with the diagnostic power of CT. Several algorithms have been proposed to address the artifacts, but most are complex and take long time to eliminate the artifacts. In the past decade, the deep learning (DL) approach has demonstrated excellent effects in image processing. In this work, we present a twostep convolutional neural networks (CNNs) that reduces the artifacts. First step uses the U-shape network (UNet) to learn and correct the low frequency artifacts. Second step uses residual network (ResNet) to extract the high frequency artifacts. Our proposed method is capable of eliminating the bone induced artifacts within a relatively low time cost. Promising results have been obtained in our experiment with a large number of CT head images.