A Deep Model for Super-resolution Enhancement from a Single Image

This paper presents a method to reconstruct a high-resolution image using a deep convolution neural network. we propose a deep model, entitled deep block super resolution (dbsr), by fusing the output features of a deep convolutional network and a shallow convolutional network. in this way, our model benefits from the high frequency and low frequency features extracted from deep and shallow networks simultaneously. we use the residual layers in our model to make repetitive layers, increase the depth of the model, and make an end-to-end model. furthermore, we employ a deep network in the up-sampling step instead of the bicubic interpolation method used in most of the previous works. since the image resolution plays an important role to obtain rich information from the medical images and helps for an accurate and a faster diagnosis of the ailment, we use the medical images for resolution enhancement. our model is capable of reconstructing a high-resolution image from a low-resolution one in both the medical and general images. the evaluation results on the tsa and tzde datasets including mri images, and set5, set14, b100, and urban100 datasets including general images demonstrate that our model outperforms the state-of-the-art alternatives in both areas of medical and general super-resolution enhancement from a single input image.