A Complexity Guidance based Dynamic Activation Network for Phase Image Processing

Motivation: Existing phase processing methods often require users to trade off between time and precision. Therefore, a phase processing network that can dynamically activate different parts based on input samples is of great research value. Goal(s): We hope that the proposed network can adaptively determine whether to begin with VOI extraction, i.e., removing the brain skull, and provide different solutions for samples of different complexity. Approach: We combine dynamic neural network and deformable convolution in the network design to realize dynamic activation and verify it on MRI phase data. Results: Our dynamic activation based network (DANet) implements adaptive phase processing and achieve competitive performance. Impact: Our methodological framework can be applied across various field related to phase signal processing, such as Optical Interferometry (OI), Magnetic Resonance Imaging (MRI), Fringe Projection Profilometry (FPP), and Interferometric Synthetic Aperture Radar (InSAR).