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Limited-angle computed tomography (CT) has arisen in some medical and industrial applications. It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limited-angle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to correct the artifacts and to restore the image. In this network, the dilated convolution layer and multi-scale pooling layer are combined to form a group and exited in the whole encoder-decoder process. Since the dilated convolutions support an exponential expansion of the receptive field without losing resolution and coverage, the obtained artifact features possess the multi-scale characteristic. Furthermore, to improve the effectiveness and accuracy of the training step, we employ a preprocessing method, which extracts image patches. Numerical experiments verify the out-performance of the proposed method compared with some conventional methods, such as Unet based deep learning,TV- and L0 -based optimization methods.

The paper proposes a new webpage programming language that is based on XQuery: XQPage. It augments the abilities of XQuery to support the Document Object Model in browser, and to support the event response mechanism of DOM objects through extending the updating facility of XQuery. In contrast to the traditional ways using XQuery in webpage currently, it does not need any host language and can be directly applied in webpage design. It enables developers to construct, modify and operate webpage in native XML processing language. The demonstration illustrates that it is flexible, efficient, easy to use and can meet the requirement of most webpage programming.