Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …

Denoising diffusion models are a powerful type of generative models used to capture
complex distributions of real-world signals. However, their applicability is limited to …

We propose Image-to-Image Schr\" odinger Bridge (I $^ 2$ SB), a new class of conditional
diffusion models that directly learn the nonlinear diffusion processes between two given …

Recently, conditional diffusion models have gained popularity in numerous applications due
to their exceptional generation ability. However, many existing methods are training …

Priors are essential for reconstructing images from noisy and/or incomplete measurements.
The choice of the prior determines both the quality and uncertainty of recovered images. We …

Diffusion model-based inverse problem solvers have demonstrated state-of-the-art
performance in cases where the forward operator is known (ie non-blind). However, the …

In this work, we investigate an intriguing and prevalent phenomenon of diffusion models
which we term as" consistent model reproducibility'': given the same starting noise input and …