The field of diffusion models and inverse problems is rapidly evolving, with a focus on improving the efficiency, accuracy, and versatility of these methods. Recent developments have centered around enhancing the performance of diffusion-based approaches, particularly in high-resolution settings and complex inverse problems. Notably, researchers have proposed innovative frameworks for diffusion inference-time density estimation and control, as well as new methods for efficient sinogram inpainting and physically-nonnegative object generation. Additionally, there has been a emphasis on addressing the challenges of domain generalization and parameter estimation in inverse problem settings. These advances have significant implications for a range of applications, from image restoration and computed tomography reconstruction to generative modeling and physically-informed simulations.

Some noteworthy papers in this area include: The paper on RNE, which introduces a flexible framework for diffusion inference-time density estimation and control, achieving promising performances in various tasks. The paper on HiSin, which proposes an efficient diffusion-based framework for high-resolution sinogram inpainting, reducing peak memory usage and inference time while maintaining accuracy. The paper on ReGuidance, which devises a simple wrapper for boosting sample quality on hard inverse problems, offering a rigorous algorithmic guarantee for diffusion posterior sampling.