Fluid Micelle Network for Image Super-Resolution Reconstruction
受流体动力学启发，将超分辨率重建中像素运动抽象为流体流动，提出流体胶束网络，通过求解有限差分方程学习残差结构，并利用Navier-Stokes方程引导边缘信息，在基准和真实显微芯片图像上优于现有方法。
Most existing convolutional neural-network-based super-resolution (SR) methods focus on designing effective neural blocks but rarely describe the image SR mechanism from the perspective of image evolution in the SR process. In this study, we explore a new research routine by abstracting the movement of pixels in the reconstruction process as the flow of fluid in the field of fluid dynamics (FD), where explicit motion laws of particles have been discovered. Specifically, a novel fluid micelle network is devised for image SR based on the theory of FD that follows the residual learning scheme but learns the residual structure by solving the finite difference equation in FD. The pixel motion equation in the SR process is derived from the Navier-Stokes (N-S) FD equation, establishing a guided branch that is aware of edge information. Thus, the second-order residual drives the network for feature extraction, and the guided branch corrects the direction of the pixel stream to supplement the details. Experiments on popular benchmarks and a real-world microscope chip image dataset demonstrate that the proposed method outperforms other modern methods in terms of both objective metrics and visual quality. The proposed method can also reconstruct clear geometric structures, offering the potential for real-world applications.