The centralized management of traditional donation platforms is difficult to meet the needs of highly trusted mechanism. The truth of fund-raising information is difficult to distinguish, and the flow of funds is not transparent. Blockchain technology has characteristics of decentralization, data not being tampered, traceability, and peer-to-peer transaction, which lays a foundation for building a trusted donation platform. Therefore, based on the blockchain technology, a donation scheme based on the Ethereum smart contract was proposed. Firstly, the fund-raising information and donation transaction events were stored on the Ethereum blockchain, and the margin mechanism was used to ensure the authenticity and traceability of the data. Meanwhile, the architecture model of the scheme was described. The smart contract algorithm Donate was proposed to replace the manual operations in order to prevent the misappropriation and long-term non-payment problems of funds. Finally, the feasibility of the scheme was validated by the trusted fund-raising and donation platform based on smart contract. Compared with the traditional fund-raising platform, it is proved that the proposed platform can prevent false fund-raising and fund misappropriation safely and effectively.

In order to further improve the resolution of existing image super-resolution methods, a High-performance Deep Convolution neural Network (HDCN) was proposed to reconstruct a fixed-scale super-resolution image. By cascading several HDCN models, the problem that many traditional models could not upscale images in alternative scale factors was solved, and a deep edge filter in the cascade process was introduced to reduce cascading errors, and highlight edge information, High-performance Cascade Deep Convolutional neural Network (HCDCN) was got. The super-resolution image reconstruction experiment was carried out on high-performance cascade deep convolution neural network (HCDCN) model on Set5 and Set14 datasets. The experimental results prove the effectiveness of introducing the deep edge-aware filter. By comparing the performance evaluation results of HCDCN method and other image super-resolution reconstruction method, the superior performance of HCDCN method is demonstrated.