Rain Removal

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 167 papers with code (317 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.

• 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.
• 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.
• Image De-raining Using a Conditional Generative Adversarial Network
  21 Jan 2017 7 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Hence, it is important to solve the problem of single image de-raining/de-snowing.
• Pre-Trained Image Processing Transformer
  1 Dec 2020 6 repositories listed
  To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
• 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.
• Progressive Image Deraining Networks: A Better and Simpler Baseline
  26 Jan 2019 4 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
• Rain Removal in Traffic Surveillance: Does it Matter?
  30 Oct 2018 4 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 6 pointer-only (licence)
  We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain…
• 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.
• SDNet: mutil-branch for single image deraining using swin
  31 May 2021 3 repositories listed
  The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
• Multi-Scale Progressive Fusion Network for Single Image Deraining
  24 Mar 2020 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive…
• NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining
  6 Dec 2019 3 repositories listed
  In this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net (NASNet), which can solve all types of rain model tasks efficiently.
• Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
  28 Nov 2017 3 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 2 pointer-only (licence)
  This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.
• MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration
  8 Jan 2025 2 repositories listed
  This variant leverages the Taylor expansion to approximate the Softmax-attention and utilizes the concept of norm-preserving mapping to approximate the remainder of the first-order Taylor expansion, resulting in a…
• Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
  12 Sep 2023 2 repositories listed
  Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.
• HINet: Half Instance Normalization Network for Image Restoration
  13 May 2021 2 repositories listed
  Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
• EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
  19 Sep 2020 2 repositories listed Syntology ran 8 of 8 samples · 0 unverified
  To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i.
• Detail-recovery Image Deraining via Context Aggregation Networks
  1 Jun 2020 2 repositories listed
  This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status?
• DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
  27 Aug 2019 2 repositories listed
  We have validated our approach on four recognized datasets (three synthetic and one real-world).
• Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
  2 Apr 2019 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner.
• Deep Joint Rain Detection and Removal from a Single Image
  25 Sep 2016 2 repositories listed
  Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
• Clearing the Skies: A deep network architecture for single-image rain removal
  7 Sep 2016 2 repositories listed
  We introduce a deep network architecture called DerainNet for removing rain streaks from an image.
• Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey
  22 May 2025 1 repository listed
  We further propose a classification framework based on network architectures and sampling strategies, helping to clearly organize existing methods.
• Degradation-Aware Feature Perturbation for All-in-One Image Restoration
  19 May 2025 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  To address this issue, motivated by the routing strategy, we propose DFPIR, a novel all-in-one image restorer that introduces Degradation-aware Feature Perturbations(DFP) to adjust the feature space to align with the…
• Unsupervised Raindrop Removal from a Single Image using Conditional Diffusion Models
  13 May 2025 1 repository listed
  Raindrop removal is a challenging task in image processing.
• Image Restoration via Multi-domain Learning
  7 May 2025 1 repository listed
  Due to adverse atmospheric and imaging conditions, natural images suffer from various degradation phenomena.
• NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
  17 Apr 2025 1 repository listed
  This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
• Content-Aware Transformer for All-in-one Image Restoration
  7 Apr 2025 1 repository listed
  In this work, we propose DSwinIR, a Deformable Sliding window Transformer for Image Restoration.
• Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image Restoration
  26 Mar 2025 1 repository listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  Transformer-based approaches have gained significant attention in image restoration, where the core component, i.
• Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining
  24 Mar 2025 1 repository listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  Specifically, to preserve more image background details while transferring rain streaks from rainy images to the unpaired clean images, we propose a novel Channel Consistency Loss (CCLoss) by introducing the Channel…
• Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal
  20 Mar 2025 1 repository listed
  To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal.

Syntology lines on 14 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.