Deeper VGG16 layers in feature losses for diffusion MRI super-resolution introduce persistent grid artifacts in images and anisotropy maps, whereas the shallowest layer preserves consistency with ground truth at high upsampling factors.
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Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI
Deeper VGG16 layers in feature losses for diffusion MRI super-resolution introduce persistent grid artifacts in images and anisotropy maps, whereas the shallowest layer preserves consistency with ground truth at high upsampling factors.