As images become one of the most significant information carriers in daily life, it is essential to transmit and store high quality images. In the case that original image is degraded, a method to extract information and restore the degraded image is needed. In this paper, a novel algorithm is proposed to restore degraded image by using sliding window and neural network. In our algorithm, first, we used the sliding window to sample the image, then put the sampled sequence into neural network for training, establishing a nonlinear mapping model between the target clear image and the blurred image. Finally, we put the degraded image into the well-trained network to restore. The simulation results and analysis of the restoration on MATLAB are also showed, it turns out the quality and clarity of the degraded image are well improved.

This article designed and implemented a remote mobile environmental monitoring system based on Socket communication technology. The system consists of clients, a server, and remote terminals. The interaction between the client and the remote terminal of the system is based on the server acting as an intermediate medium for transmission. Not only the system can move freely, without restrictions to the use of places, but also can realize the function of remote control and network data transmission between the client and the remote terminal. At the same time, the data interaction of the whole system has real-time dynamic characteristics. The experimental results turn out that the system's rapid response and stable performance achieve the remote control requirements. We believe that this system will have strong practicalities in remote environmental monitoring.

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