Blind Super-Resolution

32 papers with code • 17 benchmarks • 8 datasets

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Most implemented papers

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

xinntao/Real-ESRGAN 22 Jul 2021

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 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

yuanjunchai/IKC CVPR 2019

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.

Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation

leon-bungert/blind_remote_sensing 4 Oct 2017

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 ECCV 2020

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks.

Unsupervised Degradation Representation Learning for Blind Super-Resolution

LongguangWang/DASR 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 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 26 Feb 2022

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.

Exploiting Diffusion Prior for Real-World Image Super-Resolution

IceClear/StableSR 11 May 2023

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).

Unfolding the Alternating Optimization for Blind Super Resolution

greatlog/DAN NeurIPS 2020

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}.