Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.
Keys, Cubic convolution interpolation for digital image processing, IEEE transactions on acoustics, speech, and signal processing 29 (6) (2003) 1153–1160
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.