27 citations · 45 across the 8 of their papers we have counts for
4 papers · 1 filter
Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network
Kerem Basim, Mehmet Ozan Unal, Metin Ertas +1
Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often…
Task-Adaptive Low-Dose CT Reconstruction
Necati Sefercioglu, Mehmet Ozan Unal, Metin Ertas +1
Deep learning-based low-dose computed tomography reconstruction methods already achieve high performance on standard image quality metrics like peak signal-to-noise ratio and struc…
An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior
Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
Low-dose CT imaging requires reconstruction from noisy indirect measurements which can be defined as an ill-posed linear inverse problem. In addition to conventional FBP method in…
Self-Supervised Training For Low Dose CT Reconstruction
Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low-dose CT reconstruction has been offered with the…