Psoriasis

Deep regularization networks for inverse problems with noisy operators

A supervised learning approach is proposed for regularization of large inverse problems where the main operator is built from noisy data. This is germane to superresolution imaging via the sampling indicators of the inverse scattering theory. We aim to accelerate the spatiotemporal regularization process for this class of inverse problems to enable real-time imaging. In this approach, a neural o...

On Inverse Problems, Parameter Estimation, and Domain Generalization

Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular step in almost all physical applications, which is normally executed prior to downstream tasks that often involve parameter estimation. In this work, we analyze the general proble...

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing...

Implicit Regularization of the Deep Inverse Prior Trained with Inertia

Solving inverse problems with neural networks benefits from very few theoretical guarantees when it comes to the recovery guarantees. We provide in ...

Solving Inverse Problems with FLAIR

Flow-based latent generative models such as Stable Diffusion 3 are able to generate images with remarkable quality, even enabling photorealistic tex...

Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in ...

Deep learning-based MRI reconstruction with Artificial Fourier Transform Network (AFTNet).

Deep complex-valued neural networks (CVNNs) provide a powerful way to leverage complex number operations and representations and have succeeded in sev...

Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its ons...

EquiReg: Equivariance Regularized Diffusion for Inverse Problems

Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. In the Bayesian framework, diffusion-b...

Plug-and-Play Posterior Sampling for Blind Inverse Problems

We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image ...

Hypothesis Testing in Imaging Inverse Problems

This paper proposes a framework for semantic hypothesis testing tailored to imaging inverse problems. Modern imaging methods struggle to support hyp...

Compositional Scene Understanding through Inverse Generative Modeling

Generative models have demonstrated remarkable abilities in generating high-fidelity visual content. In this work, we explore how generative models ...

Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals

While virtual try-on (VTON) systems aim to render a garment onto a target person image, this paper tackles the novel task of virtual try-off (VTOFF)...

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models

Inverse problems (IPs) involve reconstructing signals from noisy observations. Recently, diffusion models (DMs) have emerged as a powerful framework...

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models

Inverse problems (IPs) involve reconstructing signals from noisy observations. Traditional approaches often rely on handcrafted priors, which can fa...

Dual Ascent Diffusion for Inverse Problems

Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a powerf...

Neural Inverse Scattering with Score-based Regularization

Inverse scattering is a fundamental challenge in many imaging applications, ranging from microscopy to remote sensing. Solving this problem often re...

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling

We consider the problem of sampling distributions stemming from non-convex potentials with Unadjusted Langevin Algorithm (ULA). We prove the stabili...

Stochastic Orthogonal Regularization for deep projective priors

Many crucial tasks of image processing and computer vision are formulated as inverse problems. Thus, it is of great importance to design fast and ro...

Active learning for efficient nanophotonics inverse design in large and diverse design spaces.

The diverse range of shapes enabled by modern nanofabrication techniques makes it challenging to identify the most optimal design for a desired optica...