To address the difficulties in reconstructing high-frequency information in image super-resolution reconstruction due to the lack of dependency between low-resolution and high-resolution images and the lack of order during the reconstruction of feature map， a single-image super-resolution reconstruction method based on iterative feedback and attention mechanism was proposed. Firstly， high- and low-frequency information in the image was extracted respectively by using frequency decomposition block， and the two kinds of information was processed respectively， so that the network focused on the extracted high-frequency details to increase the restoration ability of the method on image details. Secondly， through the channel-wise attention mechanism， the reconstruction focus was put on the feature channels with effective features to improve the network ability of extracting the feature map information. Thirdly， the iterative feedback idea was adopted to increase quality of the restored image in the process of repeated comparison and reconstruction. Finally， the output image was generated through the reconstruction block. The proposed method shows better performance in comparison with mainstream super-resolution methods in the 2×， 4× and 8× experiments on Set5， Set14， BSD100， Urban100 and Manga109 benchmark datasets. In the 8× experiments on Manga109 dataset， the proposed method improves Peak Signal-to-Noise Ratio （PSNR） by about 3.01 dB and 2.32 dB averagely and respectively compared to the traditional interpolation method and the Super-Resolution Convolutional Neural Network （SRCNN）. Experimental results show that the proposed method can reduce the errors in the reconstruction process and effectively reconstruct finer high-resolution images.

Labeling ordered XML documents can process XML data without accessing the data files. The present labeling schemes have achieved better results in queries, however, the labeling schemes for insertions incurs sacrifices of query performance, lower updates efficiency, and other problems. This paper proposed a new labeling scheme for insertions, EDL（Extended Dewey Labeling）, which efficiently realizes the calculations in the insertions of XML documents without degrading query performance . The conducted experiments have shown that EDL is superior to the similar labeling schemes for updates.

In order to meet the demands for both availability and processing speed, in reference to dark channel prior estimation, an image restoration algorithm based on Approximate Point Spread Function Examining (APEX) algorithm commonly used in image deblurring was proposed. Meanwhile, because different-sized images under different weather conditions have different APEX parameters, the APEX parameter value was adjusted dynamically according to sandstorm and fog degree. Furthermore, unlike other multiple images methods, the proposed algorithm needs only one image to be the input. The experimental results show that the proposed algorithm is effective in restoring images. By using color constancy algorithm, the source color components were balanced; furthermore, the visual effect of images was enhanced.