We propose a new method for solving imaging inverse problems using text-to-image latent
diffusion models as general priors. Existing methods using latent diffusion models for …

Sampling from the posterior distribution in latent diffusion models for inverse problems is
computationally challenging. Existing methods often rely on Tweedie's first-order moments …

The recent advent of diffusion models has led to significant progress in solving inverse
problems, leveraging these models as effective generative priors. Nonetheless, challenges …

Score-based generative model (SGM) has demonstrated great potential in the challenging
limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of …

Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to
reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion …

Diffusion models (DMs) are a type of potential generative models, which have achieved
better effects in many fields than traditional methods. DMs consist of two main processes …

In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image
super-resolution that yields state-of-the-art results using only a single DDIM step. We …

Defect detection plays an important role in industry quality control and process evaluation.
Learning-based computed tomography (CT) detection with 3-D volume restoration from a …

Diffusion models have become a successful approach for solving various image inverse
problems by providing a powerful diffusion prior. Many studies tried to combine the …