The core of the Blockchain as a Service(BaaS) platform is how to deploy the blockchain network on the cloud computing platform. Fabric deployment can be divided into static components and dynamic chaincodes according to the component startup time, and chaincode deployment is the core and the most complex part of Fabric cloudification. Because the Fabric has no interfaces for Kubernetes,the current solutions in the industry implement chaincode deployment through a series of auxiliary technologies, but these solutions do not incorporate the chaincodes into the Kubernetes management environment along with static components. In response to the existing problems of BaaS scheme, the following works were mainly done:1) a comprehensive study of the underlying infrastructure, especially of the Kubernetes platform with high availability in the production environment; 2) the cloud deployment of Kubernetes on Fabric was designed and implemented, especially in the chaincode part, a brand-new container control plug-in was used to realize the support for Kubernetes at the code level and complete the goal of incorporating chaincodes into Kubernetes environment management; 3) the functional computing service was used to manage the Fabric chaincodes to realize a brand-new chaincode execution mode, which means changing from the "start-wait-call-wait" mode to the "start-call-exit" mode. The above works in Fabric cloud deployment, especially in chaincode deployment management, have certain reference value for the optimization of the BaaS platform based on Fabric and Kubernetes.

To solve the problem of information loss and obvious block effect in multi-focus image fusion, a novel multi-focus image fusion method based on image matting technique was proposed. Firstly, the rough focus information of each source image was obtained by focusing measure, all of which was used to generate the trimap of the fusion image, namely, foreground, background and unknown region. Secondly, according to the trimap, the precise focus region of each source image could be gotten by using the image matting. Finally, the obtained focus regions were combined to consist of new foreground and background. And the optimal fusion of the unknown region was conducted on the basis of the foreground and background, which enhanced the correlations between the nearby pixels of the three focusing regions. The experimental results show that compared with the traditional algorithms, the proposed algorithm can acquire higher Mutual Information (MI) and edge preservation on the objective evaluation. For the subjective evaluation, it can be seen that the block effect is obviously suppressed and the visual effect is more excellent. The proposed algorithm can be applied to the object identification and computer vision to obtain optimal fusion result.