27 citations · 45 across the 8 of their papers we have counts for
5 papers · 1 filter
Prompt-Conditioned FiLM and Multi-Scale Fusion on MedSigLIP for Low-Dose CT Quality Assessment
Tolga Demiroglu, Mehmet Ozan Unal, Metin Ertas +1
We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition p…
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…
LMM-IQA: Image Quality Assessment for Low-Dose CT Imaging
Kagan Celik, Mehmet Ozan Unal, Metin Ertas +1
Low-dose computed tomography (CT) represents a significant improvement in patient safety through lower radiation doses, but increased noise, blur, and contrast loss can diminish di…
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…
Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising
Yusuf Talha Basak, Mehmet Ozan Unal, Metin Ertas +1
While Total Variation (TV) excels in noise reduction and edge preservation, its reliance on a scalar regularization parameter limits adaptivity. In this study, we present a Learnab…