Blind Super-Resolution
40 papers with code • 17 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Blind Super-Resolution
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BSD
Urban100
Set14
Set5
Manga109
BSD100
KID-F
Most implemented papers
Unfolding the Alternating Optimization for Blind Super Resolution
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations.
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution
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.
Flow-based Kernel Prior with Application to Blind Super-Resolution
JingyunLiang/FKP
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CVPR 2021
Kernel estimation is generally one of the key problems for blind image super-resolution (SR).
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet).
End-to-end Alternating Optimization for Blind Super Resolution
greatlog/DAN
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More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target Generation
yhjo09/AdaTarget
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CVPR 2021
By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images.
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
TencentARC/FAIG
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NeurIPS 2021
Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks.
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging
Multi-focus image fusion (MFIF) and super-resolution (SR) are the inverse problem of imaging model, purposes of MFIF and SR are obtaining all-in-focus and high-resolution 2D mapping of targets.
Pixel-Level Kernel Estimation for Blind Super-Resolution
JHLew/PerPix
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IEEE Access 2021
Furthermore, based on this assumption, there have been attempts to estimate the blur kernel of a given LR image, since correct kernel priors are known to be helpful in super-resolution.
