Abstract:There are some problems in the actual shale image, such as low resolution, sometimes it is difficult to meet the needs of practical applications. To tackle with this problem, a super-resolution algorithm for shale image is proposed in this paper, which is based on double deep convolutional neural networks. In this algorithm, firstly, the input image is up-sampled by the pixel domain convolution neural network; Secondly, the gradient profile information is extracted from the up-sampled image and converted by the gradient domain convolution neural network. Finally, we use the converted gradient information as a constraint to reconstruct the high-resolution image. Moreover, we introduce the Batch-Normalization and deep residual-learning to improve the training speed of the neural network. The experimental results show that compared with the some leading super-resolution algorithm, the reconstructed image has a significant improvement in subjective vision and objective evaluation, and then it is helpful for the further processing and analysis of shale image.