Super-Resolution

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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.

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

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

Subtasks archive 2025-07-28

7 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 1,627 papers with code (3,874 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.

• Generative Adversarial Networks
  10 Jun 2014 189 repositories listed Syntology ran 23 of 55 samples · 32 unverified · 18 pointer-only (licence)
  We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D…
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
  15 Sep 2016 140 repositories listed Syntology ran 16 of 72 samples · 56 unverified · 11 pointer-only (licence)
  The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution
  27 Mar 2016 80 repositories listed Syntology ran 13 of 46 samples · 33 unverified · 5 pointer-only (licence)
  We consider image transformation problems, where an input image is transformed into an output image.
• Image Super-Resolution Using Deep Convolutional Networks
  31 Dec 2014 60 repositories listed Syntology ran 7 of 27 samples · 20 unverified · 7 pointer-only (licence)
  We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
• SinGAN: Learning a Generative Model from a Single Natural Image
  2 May 2019 47 repositories listed Syntology ran 2 of 11 samples · 9 unverified · 3 pointer-only (licence)
  We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.
• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
  16 Sep 2016 47 repositories listed Syntology ran 4 of 20 samples · 16 unverified · 1 pointer-only (licence)
  This means that the super-resolution (SR) operation is performed in HR space.
• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
  1 Sep 2018 46 repositories listed Syntology ran 8 of 44 samples · 36 unverified
  To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).
• Enhanced Deep Residual Networks for Single Image Super-Resolution
  10 Jul 2017 45 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 2 pointer-only (licence)
  Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
• High-Resolution Image Synthesis with Latent Diffusion Models
  20 Dec 2021 41 repositories listed Syntology ran 19 of 28 samples · 9 unverified · 5 pointer-only (licence)
  By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
  13 Aug 2016 22 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
• Image Super-Resolution Using Very Deep Residual Channel Attention Networks
  8 Jul 2018 20 repositories listed Syntology ran 10 of 22 samples · 12 unverified · 3 pointer-only (licence)
  To solve these problems, we propose the very deep residual channel attention networks (RCAN).
• Fourier Neural Operator for Parametric Partial Differential Equations
  18 Oct 2020 19 repositories listed Syntology ran 18 of 33 samples · 15 unverified · 9 pointer-only (licence)
  The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.
• Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
  29 Jun 2016 17 repositories listed
  In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
  8 Mar 2020 16 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
• Deep Back-Projection Networks For Super-Resolution
  7 Mar 2018 16 repositories listed Syntology ran 6 of 14 samples · 8 unverified
  The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.
• Residual Dense Network for Image Super-Resolution
  24 Feb 2018 16 repositories listed Syntology ran 7 of 24 samples · 17 unverified · 1 pointer-only (licence)
  In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR.
• Accelerating the Super-Resolution Convolutional Neural Network
  1 Aug 2016 16 repositories listed Syntology ran 0 of 15 samples · 15 unverified
  As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and…
• Consistency Models
  2 Mar 2023 15 repositories listed Syntology ran 28 of 57 samples · 29 unverified · 8 pointer-only (licence)
  Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.
• Deep Image Prior
  29 Nov 2017 14 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 6 pointer-only (licence)
  In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
• Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
  23 Nov 2018 13 repositories listed Syntology ran 2 of 25 samples · 23 unverified
  Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
• Sampling Generative Networks
  14 Sep 2016 13 repositories listed Syntology ran 1 of 15 samples · 14 unverified
  We introduce several techniques for sampling and visualizing the latent spaces of generative models.
• Learning Enriched Features for Real Image Restoration and Enhancement
  15 Mar 2020 12 repositories listed Syntology ran 3 of 20 samples · 17 unverified · 3 pointer-only (licence)
  With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
• Wide Activation for Efficient and Accurate Image Super-Resolution
  27 Aug 2018 12 repositories listed Syntology ran 0 of 17 samples · 17 unverified · 2 pointer-only (licence)
  Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
• cGANs with Projection Discriminator
  15 Feb 2018 12 repositories listed Syntology ran 9 of 9 samples · 0 unverified · 6 pointer-only (licence)
  We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
• Invertible Image Rescaling
  12 May 2020 11 repositories listed Syntology ran 3 of 17 samples · 14 unverified
  High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details…
• EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
  7 May 2019 11 repositories listed Syntology ran 5 of 19 samples · 14 unverified
  In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
• SwinIR: Image Restoration Using Swin Transformer
  23 Aug 2021 9 repositories listed Syntology ran 30 of 45 samples · 15 unverified · 5 pointer-only (licence)
  In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
• Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
  25 May 2021 9 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their…
• 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.
• SRFlow: Learning the Super-Resolution Space with Normalizing Flow
  25 Jun 2020 8 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

Syntology lines on 29 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.