5 citations · 24 across the 18 of their papers we have counts for
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eess.IV2022★ 2 cited
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention
Ronglei Ji, A. Murat Tekalp
Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry…
cs.CV2022★ 1 cited
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…
cs.CV2022
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…