Single Image Dehazing

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 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

8 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 66 papers with code (130 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.

• Contrastive Learning for Compact Single Image Dehazing
  19 Apr 2021 10 repositories listed
  In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
• FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
  18 Nov 2019 4 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain…
• Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
  5 Apr 2019 3 repositories listed
  Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
• Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
  5 Oct 2018 3 repositories listed
  Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis.
• Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
  14 May 2018 3 repositories listed
  In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training.
• Deep Variational Bayesian Modeling of Haze Degradation Process
  4 Dec 2024 2 repositories listed Syntology ran 4 of 14 samples · 10 unverified
  To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing.
• Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
  1 Jan 2022 2 repositories listed
  Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image…
• Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing
  28 Feb 2020 2 repositories listed
  The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light.
• Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing
  24 Apr 2025 1 repository listed
  Single-image dehazing is an important topic in remote sensing applications, enhancing the quality of acquired images and increasing object detection precision.
• SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing
  7 Apr 2025 1 repository listed
  However, existing dehazing methods using vanilla convolution only extract features in the temporal domain and lack the ability to capture multi-directional information.
• EENet: Frequency-Aware and Spatially Multiscale Network for Single Image Dehazing
  1 Feb 2025 1 repository listed
  EENet comprises three primary modules: the frequency processing module, the spatial processing module, and the dual-domain interaction module.
• MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
  28 Dec 2024 1 repository listed
  To overcome the drawbacks, we explore two problems in Mamba-based restoration methods: i) how to design a scanning strategy preserving both locality and continuity while facilitating restoration, and ii) how to…
• HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing
  25 Sep 2024 1 repository listed
  These results underscore the significance of HazeSpace2M and our proposed framework in addressing atmospheric haze in multimedia processing.
• Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond
  20 Jul 2024 1 repository listed
  Ignoring the domain gap between different data, former de-hazing methods simply adopt multiple datasets for explicit large-scale training, which often makes the methods themselves be violated.
• HazeCLIP: Towards Language Guided Real-World Image Dehazing
  18 Jul 2024 1 repository listed
  Combined with a region-specific dehazing technique and tailored prompt sets, the CLIP model accurately identifies hazy areas, providing a high-quality, human-like prior that guides the fine-tuning process of pre-trained…
• RobustSAM: Segment Anything Robustly on Degraded Images
  13 Jun 2024 1 repository listed Syntology ran 3 of 8 samples · 5 unverified
  Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation, acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system.
• DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
  12 Jun 2024 1 repository listed
  Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images.
• Rethinking the Elementary Function Fusion for Single-Image Dehazing
  23 May 2024 1 repository listed
  This paper addresses the limitations of physical models in the current field of image dehazing by proposing an innovative dehazing network (CL2S).
• WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing
  2 Apr 2024 1 repository listed
  In this paper, we introduce WaveDH, a novel and compact ConvNet designed to address this efficiency gap in image dehazing.
• Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing
  2 Mar 2024 1 repository listed Syntology ran 14 of 27 samples · 13 unverified
  On the one hand, the difference perception between the depth maps of the dehazing result and the ideal image is proposed to promote the dehazing network to pay attention to the non-ideal areas of the dehazing.
• U-shaped Vision Mamba for Single Image Dehazing
  6 Feb 2024 1 repository listed
  Currently, Transformer is the most popular architecture for image dehazing, but due to its large computational complexity, its ability to handle long-range dependency is limited on resource-constrained devices.
• Haze Removal via Regional Saturation-Value Translation and Soft Segmentation
  7 Jan 2024 1 repository listed
  The RSVT prior is developed based on two key observations regarding the relationship between hazy and haze-free points in the HSV color space.
• Real-world image dehazing with improved joint enhancement and exposure fusion
  9 Dec 2023 1 repository listed
  The visual and quantitative comparisons between the proposed method and state-of-the-art dehazing methods show that the proposed method has better dehazing performance and has a 50% improvement in terms of the FADE…
• Fooling the Image Dehazing Models by First Order Gradient
  30 Mar 2023 1 repository listed
  In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms.
• 4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images
  28 Mar 2023 1 repository listed
  The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images…
• Curricular Contrastive Regularization for Physics-aware Single Image Dehazing
  24 Mar 2023 1 repository listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one.
• Image Restoration with Mean-Reverting Stochastic Differential Equations
  27 Jan 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration.
• Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
  6 Oct 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.
• Rethinking Performance Gains in Image Dehazing Networks
  23 Sep 2022 1 repository listed
  Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.
• LKD-Net: Large Kernel Convolution Network for Single Image Dehazing
  5 Sep 2022 1 repository listed Syntology ran 2 of 8 samples · 6 unverified
  The designed DLKCB can split the deep-wise large kernel convolution into a smaller depth-wise convolution and a depth-wise dilated convolution without introducing massive parameters and computational overhead.

Syntology lines on 8 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.