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Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS
[article]
2021
arXiv
pre-print
With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections. While these models perform well on benchmark dataset where low-resolution (LR) images are constructed from high-resolution (HR) references with known blur kernel, real image SR is more challenging when both images in the LR-HR pair are collected from real cameras. Based on existing dense
arXiv:2009.01371v2
fatcat:nwoeffzuzvhdhldltcwzpp2iwq