5 citations · 12 across the 6 of their papers we have counts for
11 papers
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…
Self-Organized Residual Blocks for Image Super-Resolution
Onur Keleş, A. Murat Tekalp, Junaid Malik +1
It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approxi…
Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression
M. Akın Yılmaz, Onur Keleş, Hilal Güven +3
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform…
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…