---
title: Synthetic Haze Dataset Overview
url: https://www.emergentmind.com/topics/synthetic-haze-dataset
type: topic
---

# Synthetic Haze Dataset Overview

A synthetic haze dataset is a curated collection of image pairs consisting of clear (ground-truth) and artificially hazed images, where haze is simulated by a controlled, physics-based model. These datasets are foundational in supervised deep learning for image dehazing, enabling the training and benchmarking of dehazing algorithms in the absence of real-world paired clean-hazy imagery. They span a broad spectrum of scenes, haze types, intensities, and photometric conditions, and are constructed via methodologies that range from analytic image formation models to photorealistic rendering in game engines and advanced domain adaptation pipelines.

## 1. Physical Basis and Synthesis Pipeline

Synthetic haze datasets are almost universally based on the Koschmieder/Atmospheric Scattering Model (ASM), formalized as:
$$
I(x) = J(x) \cdot t(x) + A \cdot (1-t(x)), \quad t(x) = \exp(-\beta d(x)),
$$
where $I(x)$ is the observed hazy image at pixel $x$, $J(x)$ the scene radiance (clear image), $A$ the atmospheric light (RGB), $t(x)$ the transmission function, $\beta$ the medium scattering coefficient, and $d(x)$ the scene depth [1712.04143].

When ground-truth depth is absent, a single-image depth estimator is often used, as in RESIDE’s OTS where the Liu et al. network provides per-pixel depth [1712.04143], or CanDY, Make3D/BSDS [1801.02892]. More recent techniques replace this with metric-accurate depth directly from 3D game engines (SimHaze [2305.16481], UNREAL-NH [2305.09533]), or decouple haze from depth (DA-HAZE [2401.07213]) by shuffling independent depth-image pairs.

Parameterization strategies for $A$ and $\beta$ include uniform or truncated-normal sampling within experimentally validated ranges (e.g., $A \sim U[0.7, 1.0]$, $\beta \sim U[0.04, 0.2]$ for outdoor traffic scenes [1712.04143], $A \sim \mathcal{N}_{trunc}(0.8, 0.05; [0.7, 0.9])$, $\beta \sim \mathcal{N}_{trunc}(0.045, 0.02; [0.02, 0.16])$ in HazyDet [2409.19833]). For multi-density datasets, haze levels are controlled via explicit $\beta$ scheduling or by learned latents in GANs [2103.06501].

## 2. Major Public Synthetic Haze Datasets

The diversity, scale, and methodology of synthetic haze datasets are summarized in the table below:

| Dataset        | Generation Method      | Size / Splits                    |
|----------------|-----------------------|----------------------------------|
| RESIDE [1712.04143]        | ASM + monocular depth or stereo RGB-D | Indoor: 13,990 / Outdoor: 72,135 / SOTS: 500 |
| SimHaze [2305.16481]       | Unreal Engine, perfect depth + ASM    | “Tens of thousands”, urban/park, multi-haze |
| 4K-HAZE [2303.15848]       | ASM on 4K, CS-Mixer depth + GU-Net GAN | >30,000 4K pairs, day/night |
| UNREAL-NH [2305.09533]     | Unreal Engine 4.27, photometric post-processing | 10,080 pairs (480×480 patches) |
| DA-HAZE [2401.07213]       | ASM; shuffled depth-image, “depth-agnostic”| 313,950 pairs, 3× GSS expansion |
| HazeSpace2M [2409.17432]   | ASM; multiple haze types/intensities   | 2,070,600 synthetic hazy, 130,193 GT |
| HazyDet [2409.19833]       | ASM, SOTA monocular depth, trunc-norm A/β | 11,000 synthetic images |
| Nighttime 3R [2008.03864]  | ASM + empirical color prior            | NHR: 8,970, NHC: 2,750×3         |
| Hazy-COCO [2103.07065]     | Inverse-MLDCP w/ regression correction| ≈118,000 COCO images, multi-density |
| Gauge Haze [2601.10537]    | Unreal Engine 5.1.1, controlled lighting | 4,796 haze + 9,590 smoke         |

Key properties include the type of depth input, scene domain (indoor/outdoor), haze density control, and the inclusion of auxiliary data (e.g., depth maps, bounding boxes, mask annotations).

## 3. Dataset Construction Methodologies

### a. Depth-Based ASM Synthesis

Classical approaches (RESIDE, CanDY, HazyDet) synthesize hazy images by applying ASM to in-the-wild imagery, using RGB-D pairs, estimated monocular depth, or stereo disparity. The accuracy of depth estimation is critical; SimHaze demonstrates improvements by avoiding monocular estimators entirely, instead rendering both clean images and ground-truth depth in Unreal Engine [2305.16481]. The canonical pipeline:
1. Acquire or estimate $d(x)$.
2. Sample parameters $A$, $\beta$.
3. Compute $t(x) = \exp(-\beta d(x))$.
4. Blend hazy image: $I(x) = J(x)t(x) + A(1-t(x))$.

### b. Engine-Based, Fully Synthetic Rendering

Game engine datasets (SimHaze, UNREAL-NH, Gauge Haze) leverage advanced photorealistic rendering to generate high-resolution clean/hazy pairs with accurate depth, illumination, and physically-based fog propagation [2305.16481, 2305.09533, 2601.10537]. This approach eliminates scale or pose inconsistencies, supports true 3D haze and occlusion, and enables batch generation of synchronized data.

### c. Physically-Informed GAN Domain Adaptation

Recent benchmarks (4K-HAZE) post-process ASM-based synthetic images with a GAN trained to map synthetic haze statistics onto the real domain (using GU-Net), closing the domain gap in pixel distribution without direct paired data [2303.15848].

### d. Haze-Type and Density Variants

Datasets are now annotated for haze types (fog, cloud, environmental), with separate models for each (HazeSpace2M [2409.17432]). Type labels derive from controlled ASM parameter sequences plus stylization (Photoshop Neural Filters, cloud overlays).

### e. Depth-Agnostic and Content-Style Disentangled Synthesis

DA-HAZE [2401.07213] disrupts the depth–haze coupling by globally shuffling depth maps before ASM synthesis. Density-aware GANs (DAS [2103.06501]) encode haze intensity as style latents, enabling smooth latent interpolation across haze strengths, directly supporting density-aware model evaluation.

## 4. Dataset Statistics, Splits, and Benchmarking

Dataset sizes range from several thousand to over two million images (HazeSpace2M [2409.17432]). Standard protocol involves:
- Training: Multiple haze variants per clean image, often covering full $A$, $\beta$ (or haze intensity/type) parameter space.
- Validation: Held-out set, matched distribution.
- Objective testing: Separate, often more “challenging,” synthetic test pairs (SOTS, DA-SOTS, HazyDet synthetic test).
- Real-world evaluation: Distinct benchmark of unpaired real hazy images, occasionally with pseudo-ground-truth or human-labeled bounding boxes (e.g., HazyDet’s RDDTS [2409.19833]).

Metrics for benchmarking include PSNR, SSIM, BRISQUE, FADE, FID/KID (synthetic/real similarity), mAP (for detection) and task-specific SSIM/accuracy for gauge-reading [2601.10537].

## 5. Advantages, Challenges, and Limitations

**Advantages:**
- Synthetic paired data enables full-reference quantitative evaluation (PSNR, SSIM).
- Parameterized control over haze properties facilitates robustness studies.
- Synthetic depth guarantees perfect ASM compliance when using engine rendering [2305.16481].

**Limitations:**
- Domain gap persists even with highly realistic simulation (SimHaze, UNREAL-NH), as evidenced by mAP/PSNR/SSIM drops when transferring from synthetic to real haze [2305.16481, 2409.19833].
- Depth estimation errors (in monocular pipelines) cause local artifacts and over/under-hazing [1712.04143].
- Most pipelines do not model non-uniform, multi-layer, or spectrally varying haze; rare phenomena (e.g., colored smog, thick smoke) may not be captured [2303.15848, 2601.10537].
- Photometric augmentations are usually limited to haze parameter sweeps; scene/semantic diversity is constrained by the underlying clean set or engine assets.

## 6. Specialized Variants and Application-Driven Datasets

- **Nighttime haze:** 3R pipeline [2008.03864], UNREAL-NH [2305.09533] add artificially colored light sources, simulate glow/flare, and employ empirical night illumination priors.
- **Detection under haze:** HazyDet [2409.19833], Hazy-COCO [2103.07065] include bounding boxes and depth maps, supporting both dehazing and downstream object detection.
- **Fine-grained haze type/level:** HazeSpace2M [2409.17432] spans multiple haze categories and intensities, supporting haze-type classification and specialized dehazing pipelines.
- **Industrial/gauge reading:** Gauge Haze [2601.10537] addresses photometric restoration in scene-specific environments via engine-generated analog instrument imagery.

## 7. Usage Guidelines and Future Directions

Synthetic haze datasets should be selected and configured to match the intended deployment domain in scene content, haze type, and density. For cross-domain deployment, depth-agnostic or domain-adapted methods (DA-HAZE [2401.07213], GAN-adapted [2303.15848], style-disentangled [2103.06501]) offer improved real-world generalization.

Current research targets more realistic multi-layer/hyperspectral haze simulation, integration of measured meteorological data, improved depth estimation, and jointly training on synthetic plus small-scale real haze (semi-supervised, domain adaptation) [2305.09533, 2008.03864]. The release of datasets such as HazeSpace2M promises to enable haze-aware vision models operating robustly across real-world atmospheric phenomena [2409.17432].

---

**References:**  
- RESIDE [1712.04143]  
- CANDY [1801.02892]  
- SimHaze [2305.16481]  
- UNREAL-NH [2305.09533]  
- 4K-HAZE [2303.15848]  
- HazyDet [2409.19833]  
- DA-HAZE [2401.07213]  
- Nighttime 3R [2008.03864]  
- HazeSpace2M [2409.17432]  
- Hazy-COCO [2103.07065]  
- Gauge Haze [2601.10537]  
- Density-aware Synthesis [2103.06501]

Source: https://www.emergentmind.com/topics/synthetic-haze-dataset