7 papers
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Badr Moufad, Albina Ilina, Hai Victor Habi +4
Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challeng…
Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance
Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus +4
Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remain…
When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
Ahmed Ghorbel, Badr Moufad, Navid Bagheri Shouraki +5
Text-driven image and video editing can be naturally cast as inpainting problems, where masked regions are reconstructed to remain consistent with both the observed content and the…
Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance
Badr Moufad, Yazid Janati, Alain Durmus +3
Classifier-Free Guidance (CFG) is a widely used technique for improving conditional diffusion models by linearly combining the outputs of conditional and unconditional denoisers. W…
A Mixture-Based Framework for Guiding Diffusion Models
Yazid Janati, Badr Moufad, Mehdi Abou El Qassime +3
Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide…
Variational Diffusion Posterior Sampling with Midpoint Guidance
Badr Moufad, Yazid Janati, Lisa Bedin +4
Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distr…