54 citations · 59 across the 10 of their papers we have counts for
11 papers
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…
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution
Cansu Korkmaz, Nancy Mehta, Radu Timofte
Recovering high-frequency details and textures from low-resolution images remains a fundamental challenge in super-resolution (SR), especially when real-world degradations are comp…
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…
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
Assuming a known degradation model, the performance of a learned image super-resolution (SR) model depends on how well the variety of image characteristics within the training set…
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan +2
Flow-based generative super-resolution (SR) models learn to produce a diverse set of feasible SR solutions, called the SR space. Diversity of SR solutions increases with the temper…
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
It is well-known that in inverse problems, end-to-end trained networks overfit the degradation model seen in the training set, i.e., they do not generalize to other types of degrad…
On the Computation of PSNR for a Set of Images or Video
Onur Keleş, M. Akın Yılmaz, A. Murat Tekalp +2
When comparing learned image/video restoration and compression methods, it is common to report peak-signal to noise ratio (PSNR) results. However, there does not exist a generally…