Image Denoising

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

21 leaderboard tables shown for this task, 21 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 21 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

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

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 509 papers with code (1,220 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.

• 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.
• Learning to See in the Dark
  4 May 2018 19 repositories listed Syntology ran 1 of 14 samples · 13 unverified · 2 pointer-only (licence)
  Imaging in low light is challenging due to low photon count and low SNR.
• 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.
• 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.
• Simple Baselines for Image Restoration
  10 Apr 2022 13 repositories listed Syntology ran 23 of 30 samples · 7 unverified · 17 pointer-only (licence)
  Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and…
• Restormer: Efficient Transformer for High-Resolution Image Restoration
  18 Nov 2021 13 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
  8 Jan 2021 12 repositories listed Syntology ran 12 of 13 samples · 1 unverified · 7 pointer-only (licence)
  In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.
• 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.
• 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.
• NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
  30 Dec 2020 9 repositories listed
  Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
  3 Aug 2017 9 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
• Multi-Stage Progressive Image Restoration
  4 Feb 2021 8 repositories listed Syntology ran 18 of 26 samples · 8 unverified · 25 pointer-only (licence)
  At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
• CycleISP: Real Image Restoration via Improved Data Synthesis
  17 Mar 2020 8 repositories listed
  This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
• FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
  11 Oct 2017 8 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
• Index Network
  11 Aug 2019 6 repositories listed
  By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data…
• Noise2Void - Learning Denoising from Single Noisy Images
  27 Nov 2018 6 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images.
• A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
  27 Apr 2022 5 repositories listed
  Unlike previous attention mechanisms that handle pixel-level, channel-level, or patch-level features, MPA focuses on features at the image level.
• SAR2SAR: a semi-supervised despeckling algorithm for SAR images
  26 Jun 2020 5 repositories listed
  A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters.
• HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
  11 May 2020 5 repositories listed
  Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with…
• Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
  5 Oct 2019 5 repositories listed Syntology ran 0 of 20 samples · 20 unverified
  This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.
• Multi-level Wavelet-CNN for Image Restoration
  18 May 2018 5 repositories listed
  With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
• Uformer: A General U-Shaped Transformer for Image Restoration
  6 Jun 2021 4 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 2 pointer-only (licence)
  Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
• Accelerated MRI with Un-trained Neural Networks
  6 Jul 2020 4 repositories listed
  Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems.
• Pyramid Real Image Denoising Network
  1 Aug 2019 4 repositories listed
  Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features.
• Unprocessing Images for Learned Raw Denoising
  27 Nov 2018 4 repositories listed
  Machine learning techniques work best when the data used for training resembles the data used for evaluation.
• Recurrent Inference Machines for Solving Inverse Problems
  13 Jun 2017 4 repositories listed
  Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference.
• Spatial-Spectral Transformer for Hyperspectral Image Denoising
  25 Nov 2022 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications.
• MAXIM: Multi-Axis MLP for Image Processing
  9 Jan 2022 3 repositories listed Syntology ran 27 of 46 samples · 19 unverified
  In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
• Improving Image Restoration by Revisiting Global Information Aggregation
  8 Dec 2021 3 repositories listed
  Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
• Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
  26 Jan 2021 3 repositories listed
  We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.

Syntology lines on 16 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.