Diffusion models for multi-dimensional computational imaging
Diffusion models have set new benchmarks for generating high-quality and diverse data. Our research explores two complementary directions: conditional diffusion models for controllable generation, and physics-aware diffusion models that incorporate imaging physics. By integrating these perspectives with new vision transformer architectures, we aim to reconstruct high-quality multi-dimensional images from limited observations.
