14 citations · 31 across the 17 of their papers we have counts for
7 papers · 1 filter
ImIR: Image-Instruction Tuning for All-in-One Image Restoration
Süleyman Aslan, Görkay Aydemir, Mısra Yavuz +5
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrain…
Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
Ahmet Rasim Emirdagi, Süleyman Aslan, Mısra Yavuz +5
Metal artifacts from high-attenuation implants severely degrade CT image quality, obscuring critical anatomical structures and posing a challenge for standard deep learning methods…
Edit2Interp: Adapting Image Foundation Models from Spatial Editing to Video Frame Interpolation with Few-Shot Learning
Nasrin Rahimi, Mısra Yavuz, Burak Can Biner +6
Pre-trained image editing models exhibit strong spatial reasoning and object-aware transformation capabilities acquired from billions of image-text pairs, yet they possess no expli…
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
M. Akin Yilmaz, Ahmet Bilican, A. Murat Tekalp
Balancing reconstruction quality versus model efficiency remains a critical challenge in lightweight single image super-resolution (SISR). Despite the prevalence of attention mecha…
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
Ahmet Bilican, M. Akın Yılmaz, A. Murat Tekalp +1
Efficiently adapting large pretrained models is critical under tight compute and memory budgets. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA achieve efficiency t…
DFPN: Deformable Frame Prediction Network
M. Akın Yılmaz, A. Murat Tekalp
Learned frame prediction is a current problem of interest in computer vision and video compression. Although several deep network architectures have been proposed for learned frame…
Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction
M. Akin Yilmaz, A. Murat Tekalp
We analyze the performance of feedforward vs. recurrent neural network (RNN) architectures and associated training methods for learned frame prediction. To this effect, we trained…