Haze Model Research Articles

Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes. To solve these problems, we propose a new haze model combining the optical scattering model and the computer graphics rendering. Based on the new haze model, we propose a high-quality and widely applicable dehazing dataset generation pipeline that does not require paired-data training and prior knowledge. We reconstruct the three-dimensional fog space with array camera and remove haze by thresholding voxel deletion. We use the Unreal Engine 5 to generate simulation datasets and the real shooting in laboratory to verify the effectiveness and the reliability of our generation pipeline. Through our pipeline, we can obtain wonderful dehaze results and dehaze datasets under various complex outdoors lighting conditions. We also propose a dehaze dataset enhancement method based on voxel control. Our pipeline and data enhancement are suitable for the latest algorithm model, these solutions can obtain better visual effects and objective indicators.

Clouds and Clarity: Revisiting Atmospheric Feature Trends in Neptune-size Exoplanets

Over the last decade, precise exoplanet transmission spectroscopy has revealed the atmospheres of dozens of exoplanets, driven largely by observatories like the Hubble Space Telescope. One major discovery has been the ubiquity of atmospheric aerosols, often blocking access to exoplanet chemical inventories. Tentative trends have been identified, showing that the clarity of planetary atmospheres may depend on equilibrium temperature. Previous work has often grouped dissimilar planets together in order to increase the statistical power of any trends, but it remains unclear from observed transmission spectra whether these planets exhibit the same atmospheric physics and chemistry. We present a reanalysis of a smaller, more physically similar sample of 15 exo-Neptune transmission spectra across a wide range of temperatures (200–1000 K). Using condensation cloud and hydrocarbon haze models, we find that the exo-Neptune population is best described by low cloud sedimentation efficiency (f sed ∼ 0.1) and high metallicity (100 × solar). There is an intrinsic scatter of ∼0.5 scale height, perhaps evidence of stochasticity in these planets’ formation processes. Observers should expect significant attenuation in transmission spectra of Neptune-size exoplanets, up to 6 scale heights for equilibrium temperatures between 500 and 800 K. With JWST's greater wavelength sensitivity, colder (<500 K) planets should be high-priority targets given their clearer atmospheres, and the need to distinguish between the “super-puffs” and more typical gas-dominated planets.

LIDN: A novel light invariant image dehazing network

With the increasing demand for efficient image dehazing solutions in computer vision algorithms, particularly for autonomous systems, a research focus on low-inference time yet high-performance dehazing techniques has emerged. Existing dehazing methods predominantly rely on either daytime or nighttime haze models, limiting their effectiveness in handling haziness under varying lighting conditions. To address this limitation, this research paper introduces the Light Invariant Dehazing Network (LIDN), an end-to-end image dehazing network consisting of four sub-modules: Feature Extractor, Deep Global Atmospheric Light Estimator, Medium Transmission Extractor, and Encoder-Decoder. The proposed model, trained using Quadruplet loss, effectively reduces artifacts and produces sharper dehazed images. Extensive experiments conducted under diverse lighting conditions demonstrate the superior performance of the proposed LIDN model compared to state-of-the-art daytime and nighttime dehazing approaches. Remarkably, the proposed model achieves these exceptional results with a runtime of only 0.24s per image, making it highly efficient than existing dehazing algorithms.

Photochemical Hazes Can Trace the C/O Ratio in Exoplanet Atmospheres

Photochemical hazes are suspected to obscure molecular features, such as water, from detection in the transmission spectra of exoplanets with atmospheric temperatures <800 K. The opacities of laboratory produced organic compounds (tholins) from Khare et al. have become a standard for modeling haze in exoplanet atmospheres. However, these tholins were grown in an oxygen-free, Titan-like environment that is very different from typical assumptions for exoplanets, where C/O ∼ 0.5. This work presents the 0.13–10 μm complex refractive indices derived from laboratory transmission measurements of tholins grown in environments with different oxygen abundances. With the increasing uptake of oxygen, absorption increases across the entire wavelength range, and a scattering feature around 6 μm shifts toward shorter wavelengths and becomes more peaked around 5.8 μm, due to a C = O stretch resonance. Using GJ 1214 b as a test case, we examine the transmission spectra of a sub-Neptune planet with C/O ratios of solar, 1, and 1000 to evaluate the effective differences between our opacities and those of Khare. For an atmosphere with solar hydrogen and helium abundances, we find a difference of 200–1500 ppm, but for high-metallicity (Z = 1000) environments, the difference may only be 20 ppm. The 1–2 μm transmission data for GJ 1214 b rule out the Titan-like haze model, and are more consistent with C/O = 1 and C/O = solar haze models. This work demonstrates that using haze opacities that are more consistent with underlying assumptions about bulk atmospheric composition are important for building self-consistent models that appropriately constrain the atmospheric C/O ratio, even when molecular features are obscured.

Single Image Haze Removal Based on a Simple Additive Model With Haze Smoothness Prior

Single image haze removal, which is to recover the clear version of a hazy image, is a challenging task in computer vision. In this paper, an additive haze model is proposed to approximate the hazy image formation process. In contrast with the traditional optical model, it regards the haze as an additive layer to a clean image. The model thus avoids estimating the medium transmission rate and the global atmospherical light. In addition, based on a critical observation that haze changes gradually and smoothly across the image, a haze smoothness prior is proposed to constrain this model. This prior assumes that the haze layer is much smoother than the clear image. Benefiting from this prior, we can directly separate the clean image from a single hazy image. Experimental results and comparisons with synthetic images and real-world images demonstrate that the proposed method outperforms state-of-the-art single image haze removal algorithms.

TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video Dehazing

Nowadays, intelligent transport systems have a major role in providing a safe and secure traffic society for passengers, pedestrians, and vehicles. However, some bad weather conditions such as haze or fog may affect the visual clarity of video footage captured by the camera. This will cause a malfunction in further video processing algorithms performed by such automated systems. This article proposes an efficient technique for estimating the atmospheric light and the transmission map in the haze model entirely in tensor domain for video dehazing. In this work, the atmospheric light is appraised using the Mie scattering principle of visible light and the temporal coherency among the frames is achieved by means of tensor algebra. Furthermore, the transmission map is computed using Low Rank Tensor Approximation (LRTA) based on Weighted Tensor Nuclear Norm (WTNN) minimization and Tensor Total Variation (TTV) regularization. WTNN minimization is used to smooth the coarse transmission map, and TTV regularization is employed to maintain spatio-temporal continuity by preserving the details of salient structures and edges. The novelty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map and atmospheric light in the tensor domain with fine-tuned regularization terms, which is not reported till now in the direction of video dehazing. Extensive experiments show that the proposed method outperforms state-of-the-art methods in video dehazing.

Remote sensing image uneven haze removal based on correction of saturation map

Remote sensing images are frequently affected by the random haze. An efficient method to remove uneven haze is proposed based on dark channel prior (DCP) corrected by saturation map and atmospheric haze model with consideration of multiple atmospheric scattering effects. First, the non-negligible atmospheric multiple scattering phenomenon is simulated by atmosphere point spread function, and the atmospheric multiple scattering haze model is established. Second, an effect method is proposed to calculate the saturation map to correct the DCP, which is used to eliminate the intensity residual of remote sensing images and re-establish the transmission calculation formula. Finally, the image is entered into the integrated restoration framework to remove the haze. Due to the correction of the saturation map, this method can effectively remove the inhomogeneous haze and retain detailed information in the haze-free areas. Compared with other excellent dehaze approaches, qualitative and quantitative experiments are carried out to demonstrate that the proposed method can effectively recover scenes in hazy regions and can retain more detailed information in haze-free regions.

Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning.

Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.

Single Image Haze Removal With Haze Map Optimization for Various Haze Concentrations

Hazy images suffer from poor visibility and possess low-contrast, degrading the visibility of the scene. The performance of single-image haze removal methods is limited by priors or constraints. This article presents an efficient single image dehazing method by cascading two models. The first model is a new atmospheric scattering model from which atmospheric light and transmission map are estimated and the second model is the proposed sparse haze model from which a haze map is estimated. A least-square optimization is carried out on the haze map to restore the haze-free image. Moreover, the analysis of the haze removal process has been investigated by a membership function to observe how much haze has been removed from the input hazy images containing various degrees of haze. Dehazing performances are evaluated on three types of datasets, i.e., real-world hazy images, synthetic images of various degrees of haze, and our developed dataset containing thick haze. Experimental results demonstrate that the proposed approach generates better performance than the state-of-the-art methods, especially in the sky or objects containing white objects, both qualitatively and quantitatively.

Multi-path dilated convolution network for haze and glow removal in nighttime images

In this paper, we address the single-image haze removal problem in nighttime scenes. The night haze removal is a severely ill-posed problem due to the presence of various visible night light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce a noticeable amount of glow in the night scenes. To overcome these effects, we introduce a deep learning-based DeGlow–DeHaze iterative model which accounts for varying colors and glows. The proposed model is a linear combination of three terms: the direct transmission attenuation, airlight and glow. First, a multi-path dilated convolution DeGlow network is introduced to interactively learn the local features with different reception fields and reduce the glow effect. The glow term is estimated by a binary mask that informs the location of the illumination source. As a result, the nighttime image is only left with only direct transmission and airlight terms. Finally, a separate post-processing DeHaze network is included to remove the haze effect from the reduced glow image. For our model training, we collected the night hazy images from internal and external resources, synthesized transmission maps from the NYU depth datasets, and consequently restored the haze-free images. The quantitative and qualitative evaluations show the effectiveness of our model on several real and synthetic images and compare our results with existing night haze models. The experimental results demonstrate that our multi-path CNN model outperforms other state-of-the-art methods in terms of PSNR (19.25 dB), SSIM (0.9958) evaluation parameters and computation time.

VROHI: Visibility Recovery for Outdoor Hazy Image in Scattering Media

Additive haze model (AHM), due to its high simplicity, has a potential to increase the efficiency of the restoration procedure of images degraded by scattering media. However, AHM is designed for hazy remote sensing data and is not suitable to be used on outdoor images. In this paper, according to the low-frequency feature (LFC) of haze, AHM is modified via gamma correction technique to make it suitable for modeling outdoor images. Benefitting from the modified AHM (MAHM), a simple yet effective method called VROHI is proposed to enhance the visibility of an outdoor hazy image. In specific, a low complexity LFC extraction method is designed by utilizing characteristic of the discrete cosine transform. Subsequently, by constructing the linear function of unknown parameters and imposing the saturation prior on MAHM, the image dehazing problem can be derived into a global optimization function. Experiments reveal that the proposed VROHI is superior to the other state-of-the-art techniques in terms of both the processing efficiency and recovery quality.

Polysaccharide selection and mechanism for prevention of protein-polyphenol haze formation in beverages.

Polysaccharides have been considered as a group of promising candidate for preventing the protein-polyphenol haze formation in beverages. In order to select effective polysaccharides to prevent the haze formation, four protein-polyphenol haze model systems were successfully established using two proteins (i.e., gelatin and bovine serum albumin) and two polyphenols (i.e., procyanidin [PC] and epigallocatechin gallate [EGCG]). Among seven common polysaccharides, 0.5mg/mL pectin, 0.05mg/mL xanthan gum, and 0.01mg/mL guar gum demonstrated the maximum potential for preventing the formation of four protein-polyphenol hazes. Ultraviolet-visible spectrophotometry confirmed that polysaccharides affected protein-polyphenol interactions. Fluorescence spectrophotometry combined with microscale thermophoresis data indicated the relative affinities of polyphenol to protein and polysaccharide determined the mechanism of polysaccharide for preventing the haze formation. In bovine serum albumin (BSA)/gelatin-EGCG system, polysaccharides (pectin, xanthan gum and guar gum) competed with BSA/gelatin to bind EGCG for prevention the formation of BSA/gelatin-EGCG haze. However, in BSA/gelatin-PC system, polysaccharides (pectin, xanthan gum, and guar gum) formed a ternary complex (protein-tannin-polysaccharide) for increasing the solubility of protein-polyphenol aggregation. From apple juice results, the reduction rates of guar gum in two apple juice systems (gelatin-PC, BSA-PC) were 21% and 56% within 8 weeks, indicating guar gum might be the most effective polysaccharide in preventing the haze formation. PRACTICAL APPLICATION: This experiment data could be used for development of polysaccharide products for prevention of protein-polyphenol haze formation in beverages.

A Featureless Infrared Transmission Spectrum for the Super-puff Planet Kepler-79d

Abstract Extremely low-density planets (“super-puffs”) are a small but intriguing subset of the transiting planet population. With masses in the super-Earth range (1 − 10 ) and radii akin to those of giant planets (&gt; 4 ), their large envelopes may have been accreted beyond the water snow line and many appear to be susceptible to catastrophic mass loss. Both the presence of water and the importance of mass loss can be explored using transmission spectroscopy. Here, we present new Hubble space telescope WFC3 spectroscopy and updated Kepler transit depth measurements for the super-puff Kepler-79d. We do not detect any molecular absorption features in the 1.1 − 1.7 μm WFC3 bandpass, and the combined Kepler and WFC3 data are consistent with a flat-line model, indicating the presence of aerosols in the atmosphere. We compare the shape of Kepler-79d’s transmission spectrum to predictions from a microphysical haze model that incorporates an outward particle flux due to ongoing mass loss. We find that photochemical hazes offer an attractive explanation for the observed properties of super-puffs like Kepler-79d, as they simultaneously render the near-infrared spectrum featureless and reduce the inferred envelope mass-loss rate by moving the measured radius (optical depth unity surface during transit) to lower pressures. We revisit the broader question of mass-loss rates for super-puffs and find that the age estimates and mass-loss rates for the majority of super-puffs can be reconciled if hazes move the photosphere from the typically assumed pressure of ∼10 mbar to bar.

Characterization of recombinant GRIP32 as a novel haze protein for protein-polyphenol haze models and prevention of haze formation with polysaccharides in the models

Haze-active proteins are an important factor in haze formation in wine and clear beverages. The aim of this study was to understand the role of haze proteins from pland origin, which are rich in basic amino acids from the plants, in the haze formation involving proteins and polyphenols, as well as to develop strategies to prevent haze formation. The grape ripening-related protein-like (GRIP) containing 12.50% histidine, 28.13% glycine, 16.41% glutamic acid and 1.56% arginine was screened as a potential haze protein from grape by bioinformatics, and recombinantly expressed, purified and characterized. The recombinant GRIP32 (rGRIP32) could be effectively removed by the adsorbents (i.e. bentonite and kaolin), or hydrolyzed by proteases (i.e. papain, neutral protease, and alkaline protease). Procyanidins (PCs) or epigallocatechin gallate (EGCG) were proved to form a haze with rGRIP32. Protein-polyphenol haze models were established using rGRIP32 at 25 °C and pH 5.0. Polysaccharides including pectin, xanthan gum, and guar gum were shown to suppress the haze caused by rGRIP32. The microscale thermophoresis (MST) assay provided new evidences for the binding of proteins and polyphenols to form haze, and demonstrated that the mechanisms of polysaccharide-based haze prevention include competitive binding of polyphenols and formation of ternary complexes with them.

Variational Single Image Dehazing for Enhanced Visualization

In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing process. Besides, the experimental study illustrates that the YUV color space is most suitable for image dehazing. Accordingly, a variational model is proposed in the Y channel of the YUV color space by combining the reformulation of the haze model and the two effective priors. As we mainly focus on the Y channel, most of the chrominance information of the image is preserved after dehazing. The numerical procedure based on the alternating direction method of multipliers (ADMM) scheme is presented to obtain the optimal solution. Extensive experimental results on real-world hazy images and synthetic dataset demonstrate clearly that our method can unveil the details and recover vivid color information, which is competitive among many existing dehazing algorithms. Further experiments show that our model also can be applied for image enhancement.

Radiance–Reflectance Combined Optimization and Structure-Guided $\ell _0$-Norm for Single Image Dehazing

Outdoor images are subject to degradation regarding contrast and color because atmospheric particles scatter incoming light to a camera. Existing haze models that employ model-based dehazing methods cannot avoid the dehazing artifacts. These artifacts include color distortion and overenhancement around object boundaries because of the incorrect transmission estimation from a depth error in the skyline and the wrong haze information, especially in bright objects. To overcome this problem, we present a novel optimization-based dehazing algorithm that combines radiance and reflectance components with an additional refinement using a structure-guided l 0 -norm filter. More specifically, we first estimate a weak reflectance map and optimize the transmission map based on the estimated reflectance map. Next, we estimate the structure-guided l 0 transmission map to remove the dehazing artifacts. The experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of qualitative and quantitative measures compared with simulated image pairs. In addition, the real-world enhancement results demonstrate that the proposed method can provide a high-quality image without undesired artifacts. Furthermore, the guided l 0 -norm filter can remove textures while preserving edges for general image enhancement algorithms.

Multiple water band detections in the CARMENES near-infrared transmission spectrum of HD 189733 b

Aims. We explore the capabilities of CARMENES for characterising hot-Jupiter atmospheres by targeting multiple water bands, in particular, those at 1.15 and 1.4 μm. Hubble Space Telescope observations suggest that this wavelength region is relevant for distinguishing between hazy and/or cloudy and clear atmospheres. Methods. We observed one transit of the hot Jupiter HD 189733 b with CARMENES. Telluric and stellar absorption lines were removed using SYSREM, which performs a principal component analysis including proper error propagation. The residual spectra were analysed for water absorption with cross-correlation techniques using synthetic atmospheric absorption models. Results. We report a cross-correlation peak at a signal-to-noise ratio (S/N) of 6.6, revealing the presence of water in the transmission spectrum of HD 189733 b. The absorption signal appeared slightly blueshifted at –3.9 ± 1.3 km s−1. We measured the individual cross-correlation signals of the water bands at 1.15 and 1.4 μm, finding cross-correlation peaks at S/N of 4.9 and 4.4, respectively. The 1.4 μm feature is consistent with that observed with the Hubble Space Telescope. Conclusions. The water bands studied in this work have been mainly observed in a handful of planets from space. Being able also to detect them individually from the ground at higher spectral resolution can provide insightful information to constrain the properties of exoplanet atmospheres. Although the current multi-band detections can not yet constrain atmospheric haze models for HD 189733 b, future observations at higher S/N could provide an alternative way to achieve this aim.

Research on haze mode countermeasures of PPP comprehensive treatment in the whole process

This paper designs third party governance market, carbon cycle trading two kinds of PPP governance haze model. Analyse the tasks of each stage (Source control, process control, end control) and the relationship between the three main bodies (government, public, haze governance corporation).In order to ensure the normal operation of PPP mode, five guarantee mechanisms have been established(Policy guarantee mechanism, Contractual credit mechanism, Negotiation competition mechanism, Win-win mechanism and Subsidy supervision incentive mechanism).The function and running process of each mechanism are determined, and the haze mode of the whole process of PPP is innovated, which has a good reference significance for the comprehensive management of haze.

Variational Low-light Image Enhancement based on a Haze Model

Variational Low-light Image Enhancement based on a Haze Model

Single Image Dehazing With Depth-aware Non-local Total Variation Regularization.

Single image dehazing can benefit many computer vision applications hence has attracted much more attention in recent years. However, it still remains a challenging task due to its double uncertainty of scene transmission and scene radiance. The existing image dehazing methods usually impair edges in the estimated transmission which leads to halo effects in the dehazing results. Besides, most existing methods suffer from noise and artifacts amplification in dense haze region after dehazing. To address these challenges, we propose a transmission adaptive regularized image recovery method for high quality single image dehazing. An initial transmission map is first obtained by a boundary constraint on the haze model. Then it is refined by applying a non-local total variation (NLTV) regularization to keep depth structures while smoothing excessive details. Noticing that the artifacts amplification effect depends on scene transmission, a transmission adaptive regularized recovery method based on NLTV is proposed to simultaneously suppress visual artifacts and preserve image details in the final dehazing result. An efficient alternating optimization algorithm is also proposed to solve the regularization model. Thorough experimental results demonstrate that the proposed method can effectively suppress visual artifacts for degraded hazy images, and yields high-quality results comparative to the state-of-the-art dehazing methods both quantitatively and qualitatively.