A Review of Remote Sensing Image Dehazing

Liu, Juping; Wang, Shiju; Wang, Xin; Ju, Mingye

doi:10.3390/s21113926

Cited by 33 publications

(16 citation statements)

References 81 publications

(98 reference statements)

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“…Liu et al. [24] classified image dehazing methods into three categories: image enhancement based on image processing algorithms with hand‐crafted features, image dehazing algorithms based on prior information or physical model, and deep learning image dehazing networks.…”

Section: Related Workmentioning

confidence: 99%

“…Image enhancement techniques have been developed aiming to restore the visibility of degraded images. Liu et al [24] classified image dehazing methods into three categories: image enhancement based on image processing algorithms with hand-crafted features, image dehazing algorithms based on prior information or physical model, and deep learning image dehazing networks. Chaudhry et al [25] proposed a framework to remove haze in outdoor images based on median filtering and Laplacian filtering.…”

Section: Image Enhancement For Survey Imagementioning

confidence: 99%

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Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Chan

Leung

et al. 2023

IET Image Processing

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Computer vision applications usually rely on the features extracted from input images with good visibility. Image acquisition systems may produce degraded images with low contrast or distorted colours. For instance, bad weather (haze, fog) can cause images captured outdoor with low visibility. Image processing algorithms generally assume that the input image is the scene radiance. Haze removal, with the recovery of image radiance, ensures reliable features extracted from images and the image processing algorithm can achieve optimal performance. Inspired by the concept of image dehazing, the authors propose an image enhancement method that can be used to improve the visibility of the images. Each original image is first transformed into multiple exposure images by means of gamma‐correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local ternary pattern. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature. The authors evaluate their proposed method on four benchmark image dehazing datasets. The quantitative results show that our method outperforms many deterministic algorithms and deep learning models. Moreover, the authors investigate the impact of image enhancement on a practical image‐based application—the reconstruction of three‐dimensional (3D) model of survey scene. Accurate 3D model reconstruction depends on high‐quality images. Degraded images will result in large errors in the reconstructed 3D model. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that when fed into the photogrammetry software, the images enhanced by the authors’ method can reconstruct 3D scene models with sub‐millimetre mean errors, which are much better than those with the original images. As shown in the visual and quantitative results of 3D model reconstruction, the authors’ proposed method also outperforms other image enhancement methods.

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Section: Related Workmentioning

confidence: 99%

Section: Image Enhancement For Survey Imagementioning

confidence: 99%

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Chan

Leung

et al. 2023

IET Image Processing

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“…Aerial images refer to photos taken from UAVs, helicopters and other aircraft, which have rich information content. Therefore, they are widely used in various fields, such as remote sensing [1], agriculture [9], geology [10] and earth science [11]. In addition, aerial images can also facilitate numerous subsequent high-level vision applications, such as target detection [12], aerial surveillance [13], scene understanding [14], land cover classification [15].…”

Section: Introductionmentioning

confidence: 99%

“…The dehazing technology of aerial images has attracted increasing attention [1], and several classical approaches have been developed. Early methods employed image enhancement strategies, such as histogram equalization [2], homomorphic filter [3], wavelet transform [4] and Retinex [5], which can effectively recover clear images.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN

Xie

2022

IEEE Access

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Aerial image dehazing is an important preprocessing step, since haze extremely degrades the imaging quality and affects subsequent the applications of aerial imagery. Most current haze removal methods achieve encouraging performance by relying on paired synthetic data, while are limited to their generality and scalability in the practical tasks. To this end, this paper aims to learn an effective unsupervised dehazing model from an unpaired set of clear and hazy aerial images. Motivated by the great advantages of contrastive learning in unsupervised representation field, we first attempt to formulate a Asymmetric Contrastive CycleGAN dehazing framework (namely ACC-GAN) to maximize the mutual information between the hazy domain and the haze-free domain. In the latent representation space, the introduced contrastive constraint ensures that the restored image is pulled closer to the clear image and pushed away from the hazy image, so as to indirectly regularize the unsupervised dehazing process. Importantly, different from the standard CycleGAN, we develop an additional feature transfer network into the forward path to form the asymmetric structure of ACC-GAN, which can enhance encoded features from hazy domain to haze-free domain. During training, multi-dimension loss terms are jointly built into a loss committee for generating dehazed results with higher naturalness and better fidelity. Experimental results on synthesis and real-world datasets indicate that our method is superior to existing unsupervised dehazing approaches, and is also very competitive to other related supervised models.INDEX TERMS aerial imagery; haze removal; asymmetric CycleGAN; unsupervised learning; contrastive learning.

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Dehazing of Satellite Images with Low Wavelength and High Distortion: A Comparative Analysis

Sajeevan,

Sabarish

2023

Smart Innovation, Systems and Technologies

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A Review of Remote Sensing Image Dehazing

Cited by 33 publications

References 81 publications

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN

Dehazing of Satellite Images with Low Wavelength and High Distortion: A Comparative Analysis

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