Latest AI and machine learning research in psoriasis for healthcare professionals.
Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as image reconstruction under noisy or undersampled conditions. Meanwhile, tensor decomposition has gained prominence in federated learning (FL) due to its effectiveness in exploiting latent space structure and its capaci...
Iterative Krylov projection methods have become widely used for solving large-scale linear inverse problems. However, methods based on orthogonality include the computation of inner-products, which become costly when the number of iterations is high; are a bottleneck for parallelization; and can cause the algorithms to break down in low precision due to information loss in the projections. Recen...
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness reg...
Learning diffusion bridge models is easy; making them fast and practical is an art. Diffusion bridge models (DBMs) are a promising extension of diff...
The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditiona...
Learning effective regularization is crucial for solving ill-posed inverse problems, which arise in a wide range of scientific and engineering appli...
Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is ut...
Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurate...
Imaging inverse problems can be solved in an unsupervised manner using pre-trained diffusion models, but doing so requires approximating the gradien...
Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generativ...
Diffusion models have indeed shown great promise in solving inverse problems in image processing. In this paper, we propose a novel, problem-agnosti...
Consistent improvement of image priors over the years has led to the development of better inverse problem solvers. Diffusion models are the newcome...
Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical difficulty o...
We focus on designing and solving the neutral inclusion problem via neural networks. The neutral inclusion problem has a long history in the theory ...
Maximum entropy methods, based on the inverse Ising/Potts problem from statistical mechanics, are essential for modeling interactions between pairs ...
Network services are increasingly managed by considering chained-up virtual network functions and relevant traffic flows, known as the Service Funct...
Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achi...
To perform image editing based on single-view, inverse physically based rendering, we present a method combining a learning-based approach with prog...
Visual perception in the brain largely depends on the organization of neuronal receptive fields. Although extensive research has delineated the codi...
Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limit...