Despite the visually-pleasing results achieved, the massive computational cost has been a
long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits …

Diffusion models are rising as a powerful solution for high-fidelity image generation, which
exceeds GANs in quality in many circumstances. However, their slow training and inference …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …

MRI and CT are most widely used medical imaging modalities. It is often necessary to
acquire multi-modality images for diagnosis and treatment such as radiotherapy planning …

Structural topology optimization, which aims to find the optimal physical structure that
maximizes mechanical performance, is vital in engineering design applications in …

Abstract Diffusion Probabilistic Models (DPMs) have recently been employed for image
deblurring, formulated as an image-conditioned generation process that maps Gaussian …

The multi-source stationary CT, where both the detector and X-ray source are fixed,
represents a novel imaging system with high temporal resolution that has garnered …

Latest diffusion-based methods for many image restoration tasks outperform traditional
models, but they encounter the long-time inference problem. To tackle it, this paper …

Abstract This paper proposes Retrieval-Enhanced Asymmetric Diffusion (READ) for image-
based robot motion planning. Given an image of the scene READ retrieves an initial motion …

The field of image generation has made significant progress thanks to the introduction of
Diffusion Models, which learn to progressively reverse a given image corruption. Recently, a …