Focused on the problems of Single Image Super-Resolution (SISR) reconstruction methods, such as the loss of high frequency information during the process of image reconstruction, the introduction of noise during the process of upsampling and the difficulty of determining the interdependence relationships between the channels of the feature map, a deep progressive back-projection attention network was proposed. Firstly, a progressive upsampling method was used to gradually scale the Low Resolution (LR) image to a given magnification in order to alleviate problems such as high-frequency information loss caused by upsampling. Then, at each stage of progressive upsampling, iterative back-projection idea was merged to learn mapping relationship between High Resolution (HR) and LR feature maps and reduce the introduced noise in the upsampling process. Finally, the attention mechanism was used to dynamically allocate attention resources to the feature maps generated at different stages of the progressive back-projection network, so that the interdependence relationships between the feature maps were learned by the network model. Experimental results show that the proposed method can increase the Peak Signal-to-Noise Ratio (PSNR) by up to 3.16 dB and the structural similarity by up to 0.218 4.

In the process of improving discriminant ability of dictionary, max-margin dictionary learning methods ignore that the generalization of classifiers constructed by reacquired data is not only in relation to the principle of maximum margin, but also related to the radius of Minimum Enclosing Ball (MEB) containing all the data. Aiming at the fact above, Generalization Error Bound Guided discriminative Dictionary Learning (GEBGDL) algorithm was proposed. Firstly, the discriminant condition of Support Vector Guided Dictionary Learning (SVGDL) algorithm was improved based on the upper bound theory of about the generalization error of Support Vector Machine (SVM). Then, the SVM large margin classification principle and MEB radius were used as constraint terms to maximize the margin between different classes of coding vectors, and to minimum the MEB radius containing all coding vectors. Finally, as the generalization of classifier being better considered, the dictionary, coding coefficients and classifiers were updated respectively by alternate optimization strategy, obtaining the classifiers with larger margin between the coding vectors, making the dictionary learn better to improve dictionary discriminant ability. The experiments were carried out on a handwritten digital dataset USPS, face datasets Extended Yale B, AR and ORL, object dataset Caltech 101, COIL20 and COIL100 to discuss the influence of hyperparameters and data dimension on recognition rate. The experimental results show that in most cases, the recognition rate of GEBGDL is higher than that of Label Consistent K-means-based Singular Value Decomposition (LC-KSVD), Locality Constrained and Label Embedding Dictionary Learning (LCLE-DL), Fisher Discriminative Dictionary Learning (FDDL) and SVGDL algorithm, and is also higher than that of Sparse Representation based Classifier (SRC), Collaborative Representation based Classifier (CRC) and SVM.

Aiming at the main problem of the Sparse Representation (SR) coefficients of the image blocks in image super-resolution method, an enhanced self-learning super-resolution approach for single image was proposed by using the weighting idea. Firstly, the pyramid of high and low resolution images was established by self-learning. Then, the image block feature of low-resolution images and the central pixels of the corresponding high-resolution image blocks were extracted respectively. The role of the center pixel in constructing the image block sparse coefficient was emphasized by giving different weights of different pixels in the image blocks. Finally, the combination of SR theory and Support Vector Regression (SVR) technique was used to build the super-resolution image reconstruction model. The experimental results show that compared with the Self-Learning Super-Resolution method for single image (SLSR), the Peak Signal-to-Noise Ratio (PSNR) of the proposed method is increased by an average of 0.39 dB, the BRISQUE (Blind/Reference-less Image Spatial Quality Evaluator) score of no-reference image quality evaluation criteria is reduced by an average of 9.7. From the subjective perspective and objective values, it is proved that the proposed super resolution method is more effective.

The scheme proposed by Zhou et al. (ZHOU M, MU Y, SUSILO W, et al. Privacy enhanced data outsourcing in the cloud. Journal of network and computer applications, 2012, 35(4): 1367-1373) was analyzed, and the shortcoming of no access privilege revocation was shown. To address the shortcoming, an outsourced data encryption scheme with revoking access privilege was proposed. Firstly, the data were divided into several data blocks, and each data block was encrypted separately. Secondly, with the key derivation method, the number of keys stored and managed by the data owner was reduced. Finally, multiple decryption keys were constructed on an encrypted data to revoke access privileges of some users, without affecting the legitimate users. Compared with Zhou's scheme, the proposed scheme not only maintains the advantage of privacy protection to the outsourced data in the scheme, but also realizes access privilege revocation for users. The analysis results show that the proposed scheme is secure under the assumption of the Discrete Logarithm Problem (DLP).

To deal with the channel uncertainty of the correlated block fading channels, a joint estimation-decoding approach based on Factor Graph Expectation Maximization (FGEM) algorithms was proposed. In the receiver, a message passing method on factor graph was adopted to jointly estimate the channel and decode the message. EM algorithm was used to remove the effect of loops on the convergence of message passing. It also solved the calculation problem of Gaussian mixture message. The calculation of message passing was simplified by the Kalman forward-backward algorithm, which resulted in reduced complexity in joint estimation-decoding. The simulation results show that the proposed algorithm can improve the accuracy of the channel estimation and improve the decoding performance.