Blind Super-Resolution

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

18 leaderboard tables shown for this task, 18 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 18 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

9 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 44 papers with code (67 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
  22 Jul 2021 8 repositories listed Syntology ran 3 of 9 samples · 6 unverified
  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
  14 Sep 2019 4 repositories listed Syntology ran 5 of 12 samples · 7 unverified · 3 pointer-only (licence)
  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.
• Exploiting Diffusion Prior for Real-World Image Super-Resolution
  11 May 2023 3 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).
• Blind Super-Resolution With Iterative Kernel Correction
  6 Apr 2019 3 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  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.
• Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
  15 Jan 2025 2 repositories listed
  Meanwhile, another line of research focusing on rectifying the reverse process of diffusion models (i.
• End-to-end Alternating Optimization for Real-World Blind Super Resolution
  17 Aug 2023 2 repositories listed Syntology ran 10 of 15 samples · 5 unverified · 15 pointer-only (licence)
  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.
• Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
  26 Feb 2022 2 repositories listed
  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…
• Deep Constrained Least Squares for Blind Image Super-Resolution
  15 Feb 2022 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
• Unsupervised Degradation Representation Learning for Blind Super-Resolution
  1 Apr 2021 2 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
• Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
  16 Mar 2020 2 repositories listed
  Super-resolution and denoising are ill-posed yet fundamental image restoration tasks.
• Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
  4 Oct 2017 2 repositories listed
  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.
• Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
  17 Jun 2025 1 repository listed Syntology ran 6 of 12 samples · 6 unverified
  In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to paired degraded and ground-truth images -- the proposed method operates under minimal assumptions and…
• RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
  4 Dec 2024 1 repository listed
  We posit that introducing reward feedback learning to finetune the existing models can further improve the quality of the generated images.
• Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
  13 Jun 2024 1 repository listed
  Learning-based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required.
• A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
  24 Apr 2024 1 repository listed Syntology ran 11 of 19 samples · 8 unverified
  This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem.
• AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
  2 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs.
• Deep Blind Super-Resolution for Satellite Video
  13 Jan 2024 1 repository listed Syntology ran 17 of 20 samples · 3 unverified · 20 pointer-only (licence)
  Therefore, this paper proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixel-wise blur levels in a coarse-to-fine manner.
• Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
  18 Oct 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space.
• Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
  6 Oct 2023 1 repository listed
  In particular, we integrate both CNN and Transformer components into the SR network, where we first use the CNN modulated by the degradation information to extract local features, and then employ the degradation-aware…
• S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
  16 Aug 2023 1 repository listed
  In this paper, we propose a double-win framework for ideal and blind SR task, named S2R, including a light-weight transformer-based SR model (S2R transformer) and a novel coarse-to-fine training strategy, which can…
• StarSRGAN: Improving Real-World Blind Super-Resolution
  30 Jul 2023 1 repository listed
  The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior knowledge of the degradation process that caused the image to be low-resolution.
• Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment
  4 Jun 2023 1 repository listed
  In this paper, we reveal that the scale factor has a statistically significant impact on subjective quality scores of SR images, indicating that the scale information can be used to guide the task of blind SR IQA.
• Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs
  20 May 2023 1 repository listed
  In our work, we introduce conditional denoising diffusion probabilistic models (DDPM) from two aspects: kernel estimation progress and re-construction progress, named as the dual-diffusion.
• Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
  24 Feb 2023 1 repository listed
  This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks.
• Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
  15 Dec 2022 1 repository listed
  Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images.
• Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
  3 Dec 2022 1 repository listed
  Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation.
• Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
  30 Nov 2022 1 repository listed
  It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network.
• The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
  9 Nov 2022 1 repository listed
  To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to…
• KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
  21 Sep 2022 1 repository listed Syntology ran 8 of 10 samples · 2 unverified · 10 pointer-only (licence)
  Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network…
• Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
  29 Aug 2022 1 repository listed
  Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the \textit{inconsistency} between degradation embedding and SR feature is still challenging.

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.