FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.
Directing mamba to complex textures: An efficient texture- aware state space model for image restoration,
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FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.