1 code implementation • 16 Nov 2021 • Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao, Hua Huang
Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples.
1 code implementation • 29 Mar 2021 • Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao, Hua Huang
Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable degradation priors.
1 code implementation • 28 Sep 2020 • Yuanfei Huang, Jie Li, Xinbo Gao, Yanting Hu, Wen Lu
To solve them, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose on improving the detail fidelity, instead of blindly designing or employing the deep CNNs architectures for merely feature representation in local receptive fields.
no code implementations • 28 Sep 2018 • Yanting Hu, Jie Li, Yuanfei Huang, Xinbo Gao
To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features.
no code implementations • 24 Feb 2018 • Yanting Hu, Xinbo Gao, Jie Li, Yuanfei Huang, Hanzi Wang
To improve information flow and to capture sufficient knowledge for reconstructing the high-frequency details, we propose a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner.