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Super-resolution structured illumination microscopy (SIM) routinely performs image reconstruction in the frequency domain using an approach termed frequency-domain reconstruction (FDR). Due to multiple Fourier transforms between the spatial and frequency domains, SIM suffers from low reconstruction speed, constraining its applications in real-time, dynamic imaging. To overcome this limitation, we developed a new method for SIM image reconstruction, termed spatial domain reconstruction (SDR). SDR is intrinsically simpler than FDR, does not require Fourier transforms and the theory predicts it to be a rapid image reconstruction method. Results show that SDR reconstructs a super-resolution image 7-fold faster than FDR, producing images that are equal to either FDR or the widely-used FairSIM. We provide a proof-of-principle using mobile fluorescent beads to demonstrate the utility of SDR in imaging moving objects. Consequently, replacement of the FDR approach with SDR significantly enhances SIM as the desired method for live-cell, instant super-resolution imaging. This means that SDR-SIM is a “What You See Is What You Get” approach to super-resolution imaging.

In the context of increasingly tight energy supply and rising prices, it is of great significance to carry out research on energy consumption prediction models with energy conservation as the goal. In order to improve energy efficiency, it is not only necessary to conduct statistics and analysis on energy historical data, but also to predict future energy data. In this paper, Bicubic interpolation algorithm and convolutional neural network are used to spatially predict energy consumption. The model framework structure for energy consumption prediction is given, and the two models are experimentally analyzed. The results show that the convolutional neural network is better than the Bicubic interpolation method, and the prediction result is closer to the actual value. For the high spatial complexity of energy consumption data, the definition and framework of the RessNetGAN model are first given. Secondly, the specific structure of the generator is given. How to extract the spatial eigenvalue of energy consumption through 3D convolution operation is introduced, and the experiment is carried out by changing the pooling parameters of the energy consumption to be predicted. The results show that the RessNetGAN model with generated confrontation structure has better prediction performance than the convolutional neural network model, and when the input energy consumption data is very low precision, there will be no serious distortion of the prediction result.