14 citations · 31 across the 17 of their papers we have counts for
4 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…
Edit2Restore:Few-Shot Image Restoration via Parameter-Efficient Adaptation of Pre-trained Editing Models
Mustafa Akın Yılmaz, Ahmet Bilican, Burak Can Biner +1
Image restoration has traditionally required training specialized models on thousands of paired examples per degradation type. Large pre-trained text-conditioned image editing mode…