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
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Blind Super-Resolution Kernel Estimation using an Internal-GAN
sefibk/KernelGAN
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NeurIPS 2019
Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed 'ideal' downscaling kernel (e. g. Bicubic downscaling).
Blind Super-Resolution With Iterative Kernel Correction
In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown.
Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality.
Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
majedelhelou/SFM
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ECCV 2020
Super-resolution and denoising are ill-posed yet fundamental image restoration tasks.
Unsupervised Degradation Representation Learning for Blind Super-Resolution
LongguangWang/DASR
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CVPR 2021
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
Deep Constrained Least Squares for Blind Image Super-Resolution
megvii-research/dcls-sr
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CVPR 2022
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
chaofengc/femasr
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Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
End-to-end Alternating Optimization for Real-World Blind Super Resolution
greatlog/realdan
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To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model.
