Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Recent years have seen significant advancements in image restoration, largely attributed to
the development of modern deep neural networks, such as CNNs and Transformers …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Diffusion-based image super-resolution (SR) methods are mainly limited by the low
inference speed due to the requirements of hundreds or even thousands of sampling steps …

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to
displace many in the professional artist community. In particular, models can learn to mimic …

Diffusion models have been recently studied as powerful generative inverse problem
solvers, owing to their high quality reconstructions and the ease of combining existing …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …