5 citations · 24 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression
Ogun Kirmemis, A. Murat Tekalp
Rate-distortion optimization (RDO) of codecs, where distortion is quantified by the mean-square error, has been a standard practice in image/video compression over the years. RDO s…
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
A. Murat Tekalp, Michele Covell, Radu Timofte +1
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the stat…