Concerning the problem of high energy consumption existing in Cloud storage Content Distribution Network (CCDN), the energy consumption optimization method for the CCDN was studied. Firstly, the operation principle of CCDN was analysed. Then, the energy consumption formulas were given for each cloud server and each network link. Moreover, the weighted graph was used to describe the whole network. Furthermore, based on the weighted graph, an energy consumption optimization algorithm named Min-Energy-Graph (MEG) was designed to satisfy the Quality of Service (QoS) of CCDN and data distribution of network system. MEG was compared with Greedy Site (GS) and Optimal Static Placement and Routing (OSPR) by the simulation experiments. Compared with the GS and OSPR, the energy consumption of MEG was reduced by 6.6% and 30% respectively in the experiment of system extension, the energy consumption of MEG was reduced by 28.9% and 60.2% separately in the experiment of ensuring the user QoS, and the energy consumption of MEG was reduced by 32.2% and 89.3% independently in the experiment of network topology density. The experimental results show that the proposed energy management method can greatly reduce the energy consumption of CCDN.

The traditional patch-based image completion algorithms circularly search the most similar patches in the whole image, and are easily affected by confidence factor in the process of structure propagation. As a result, these algorithms have poor efficiency and need a lot of time for the big computation. To overcome these shortages, a fast image completion algorithm based on randomized correspondence was proposed. It adopted a randomized correspondence algorithm to search the sample regions, which have similar structure and texture with the target region, so as to reduce the search space. Meanwhile, the method of computing filling priorities based on confidence factor and edge information was optimized to enhance the correctness of structure propagation. In addition, the method of calculating the most similar patches was improved. The experimental results show that, compared with the traditional algorithms, the proposed approach can obtain 5-10 times speed-up in repair rate, and performs better in image completion.

With reference to the properties of multiscale and shift invariance of nonsubsampled Contourlet transform, and concerning the characteristics of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, a medical images fusion method was proposed．The proposed method fused the low frequency subband and high frequency subband of these medical images separately by the regional feature strategy. The paper introduced the judgment criteria of images fusion and expatiated on the principle and implementation of Nonsubsampled Contourlet Transform (NSCT). And this gave the subjective judgment and numeric measurement of the fusion images based on visual effect and information indexes. To evaluate the performance of the proposed algorithm, the authors compared the results with those of the algorithms, such as wavelet transform and Contourlet transform. The CT and MRI images simulation results of mandibular system indicate that the proposed method outperforms the others in terms of both visual quality and objective evaluation criteria, while it can integrate and maintain much more effective and detailed information as well.

Concerning the faults of local extremum in image registration based on mutual information, a new medical image registration method based on Powell and improved genetic algorithm was proposed in this paper. It put forward an improved method regarding the shortcomings of the standard genetic algorithm, such as slow convergence and prematurity that will result in artifacts, and generated the iteration individual by Logistic chaos map. This method utilized the multi-resolution analysis strategy and searched for the optimal of the objective function by this hybrid optimized algorithm in the lowest resolution image level. Then it continued the optimization course and accomplished the image registration by this optimal data with the Powell algorithm. The experimental results indicate that this algorithm can effectively improve the image registration velocity and avoid local extremum of the operator while getting better performance of image precision in contrast to the Powell algorithm and unimproved genetic algorithm.