Scanning Transmission Electron Microscopy (STEM) enables the observation of atomic arrangements at sub-angstrom resolution, allowing for atomically resolved analysis of the physical and chemical properties of materials. However, due to the effects of noise, electron beam damage, sample thickness, etc, obtaining satisfactory atomic-level images is often challenging. Enhancing STEM images can reveal clearer structural details of materials. Nonetheless, existing STEM image enhancement methods usually overlook unique features in the frequency domain, and existing datasets lack realism and generality. To resolve these issues, in this paper, we develop noise calibration, data synthesis, and enhancement methods for STEM images. We first present a STEM noise calibration method, which is used to synthesize more realistic STEM images. The parameters of background noise, scan noise, and pointwise noise are obtained by statistical analysis and fitting of real STEM images containing atoms. Then we use these parameters to develop a more general dataset that considers both regular and random atomic arrangements and includes both HAADF and BF mode images. Finally, we design a spatial-frequency interactive network for STEM image enhancement, which can explore the information in the frequency domain formed by the periodicity of atomic arrangement. Experimental results show that our data is closer to real STEM images and achieves better enhancement performances together with our network. Code will be available at https://github.com/HeasonLee/SFIN.

We present a novel fluid control method that is capable of driving particle-based fluid simulation to match a rapidly changing target while keeping natural fluid-like motion. To achieve the desired behavior, we first generate control particles by sampling the target shape and then apply a non-linear constraint to each control particle, with its neighboring fluid particles keeping a constant fluid density within its influence region. This density constraint is highly in line with the incompressible nature of the fluid, which can drive the fluid to match the target shape in a natural way. In addition, to match a fast moving or deforming target, we add an adaptive spring for each fluid particle in the control region, connecting with its nearest control particle. The spring constraint takes effect only when the fluid particle is far from its corresponding control particle to avoid introducing artificial viscosity. Therefore, the fluid particles are well controlled even if the target shape changes rapidly. Furthermore, we integrate a velocity constraint to adjust the stiffness of the controlled fluid. All these three constraints are solved under position-based framework which enables our simulation fast, robust and well-suitable for interactive applications. We demonstrate the efficiency and effectiveness of our method in various scenarios in real time.