54 citations · 72 across the 12 of their papers we have counts for
12 papers
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…
Image-Difficulty-Aware Evaluation of Super-Resolution Models
Atakan Topaloglu, Ahmet Bilican, Cansu Korkmaz +1
Image super-resolution models are commonly evaluated by average scores (over some benchmark test sets), which fail to reflect the performance of these models on images of varying d…
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models
Cansu Korkmaz, Ahmet Murat Tekalp, Zafer Dogan
Super-resolution (SR) is an ill-posed inverse problem with many feasible solutions consistent with a given low-resolution image. On one hand, regressive SR models aim to balance fi…
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution
Cansu Korkmaz, A. Murat Tekalp
Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on…
NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results
Zheng Chen, Zongwei Wu, Eduard Zamfir +85
This paper reviews the NTIRE 2024 challenge on image super-resolution (4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating…
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
Super-resolution (SR) is an ill-posed inverse problem, where the size of the set of feasible solutions that are consistent with a given low-resolution image is very large. Many alg…
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback
Cansu Korkmaz, Ege Cirakman, A. Murat Tekalp +1
Super-resolution (SR) is an ill-posed inverse problem with a large set of feasible solutions that are consistent with a given low-resolution image. Various deterministic algorithms…
Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression
M. Akın Yılmaz, O. Ugur Ulas, A. Murat Tekalp
The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances t…
Flexible-Rate Learned Hierarchical Bi-Directional Video Compression With Motion Refinement and Frame-Level Bit Allocation
Eren Cetin, M. Akin Yilmaz, A. Murat Tekalp
This paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bi-directional video compression to further advance the state-of-the-ar…
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
M. Akın Yılmaz, A. Murat Tekalp
Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and ent…
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression
M. Akin Yilmaz, A. Murat Tekalp
Conventional video compression methods employ a linear transform and block motion model, and the steps of motion estimation, mode and quantization parameter selection, and entropy…