Super Resolution Based on Deep Operator Networks

Abstract

We use Deep Operator Networks (DeepONets) to perform super-resolution reconstruction of the solutions of two types of partial differential equations and compare the model predictions with the results obtained using conventional interpolation methods to verify the advantages of DeepONets. We employ two pooling methods to downsample the origin data and conduct super-resolution reconstruction under three different resolutions of input images. The results show that the DeepONet model can predict high-frequency oscillations and small-scale structures from low-resolution inputs very well. For the two-dimensional problem, we introduce convolutional layers to extract information from input images at a lower cost than purer MLPs. We adjust the size of the training set and observe the variation of prediction errors. In both one-dimensional and two-dimensional cases, the super-resolution reconstruction using the DeepONet model demonstrates much more accurate prediction results than cubic spline interpolation, highlighting the superiority of operator learning methods in handling such problems compared to traditional interpolation techniques.