1 citations · 1 across the 10 of their papers we have counts for
11 papers
Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
Merve Gülle, Junno Yun, Yaşar Utku Alçalar +1
Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiti…
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle +2
MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-on…
UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
Mahdi Saberi, Toygan Kiliç, Mehmet Akçakaya
MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task…
UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction
Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya
Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks. DiTs comprise isotropic tran…
Time-Embedded Algorithm Unrolling for Computational MRI
Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya
Algorithm unrolling methods have proven powerful for solving the regularized least squares problem in computational magnetic resonance imaging (MRI). These approaches unfold an ite…
No Alignment Needed for Generation: Learning Linearly Separable Representations in Diffusion Models
Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya
Efficient training strategies for large-scale diffusion models have recently emphasized the importance of improving discriminative feature representations in these models. A centra…
Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Yaşar Utku Alçalar, Junno Yun, Mehmet Akçakaya
Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility all…
Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
Jarvis Haupt, Qin Lu, Yanning Shen +5
Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generat…
PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems
Merve Gülle, Junno Yun, Yaşar Utku Alçalar +1
Diffusion models have found extensive use in solving inverse problems, by sampling from an approximate posterior distribution of data given the measurements. Recently, consistency…
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
Yaşar Utku Alçalar, Yu Cao, Mehmet Akçakaya
Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resoluti…
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images
Junno Yun, Mehmet Akçakaya
Infrared (IR) imaging is commonly used in various scenarios, including autonomous driving, fire safety and defense applications. Thus, semantic segmentation of such images is of gr…