Single Remote Sensing Image Dehazing Using Gaussian and Physics-Guided Process

Bie, Yuxia; Yang, Siqi; Huang, Yufeng

doi:10.1109/lgrs.2022.3177257

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…In recent years, some dehazing methods tailored for satellite images [18][19][20] are explored. Jiang et al [33] introduced an empirical haze removal method for visible remote sensing images by applying an additive haze model.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…Jiang et al [33] introduced an empirical haze removal method for visible remote sensing images by applying an additive haze model. Bie et al [18] proposed a Gaussian and physics-guided dehazing network (GPD-Net) to better extract hazy features and guide the model to real-world conditions. Beyond single-stage networks, Li and Chen [19] presented a two-stage dehazing network (FCTF-Net) for haze removal tasks on satellite images by performing coarse dehazing and then refining the results for enhanced performance.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…For instance, DehazeFormer [8] exemplifies this trend with its innovative normalization layer and spatial information aggregation scheme. In the past two years, some dehazing methods tailored for satellite images [18][19][20][21][22] are explored, promoting better applications of satellite images and sharpening our view from space.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Semantic-Guided Contextual Structure-Aware Network for Satellite Image Dehazing

Yang,

Cao,

Wang

et al. 2024

Preprint

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Haze always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for satellite image dehazing. Specifically, a hybrid CNN-transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage, so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrate that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

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Hierarchical Semantic-Guided Contextual Structure-Aware Network for Satellite Image Dehazing

Yang,

Cao,

Wang

et al. 2024

Preprint

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“…With the advancement of deep learning, numerous learning-based methods have emerged and been applied in noise suppression, image enhancement, and image dehazing [16,17]. Bie et al introduced the GPD-Net (Gaussian and physics-guided dehazing network) method [18], which employs a Gaussian process in the intermediate latent space to aid in the recovery of clear images. Additionally, it incorporates physical prior information to refine the dehazing results.…”

Section: Related Workmentioning

confidence: 99%

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network

Zhao,

Yin,

Zhong

et al. 2023

Sensors

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The degradation of visual quality in remote sensing images caused by haze presents significant challenges in interpreting and extracting essential information. To effectively mitigate the impact of haze on image quality, we propose an unsupervised generative adversarial network specifically designed for remote sensing image dehazing. This network includes two generators with identical structures and two discriminators with identical structures. One generator is focused on image dehazing, while the other generates images with added haze. The two discriminators are responsible for distinguishing whether an image is real or generated. The generator, employing an encoder–decoder architecture, is designed based on the proposed multi-scale feature-extraction modules and attention modules. The proposed multi-scale feature-extraction module, comprising three distinct branches, aims to extract features with varying receptive fields. Each branch comprises dilated convolutions and attention modules. The proposed attention module includes both channel and spatial attention components. It guides the feature-extraction network to emphasize haze and texture within the remote sensing image. For enhanced generator performance, a multi-scale discriminator is also designed with three branches. Furthermore, an improved loss function is introduced by incorporating color-constancy loss into the conventional loss framework. In comparison to state-of-the-art methods, the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index metrics. These results convincingly demonstrate the superior performance of the proposed method in effectively removing haze from remote sensing images.

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“…At first, the proposed image haze removal algorithms were all used for outdoor images, and then have been slowly applied to remote sensing images [ 20 , 24 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. In a broad sense, outdoor images can also be considered as remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Huang

Zhang

2023

IJERPH

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Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value.

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Single Remote Sensing Image Dehazing Using Gaussian and Physics-Guided Process

Cited by 13 publications

References 23 publications

Hierarchical Semantic-Guided Contextual Structure-Aware Network for Satellite Image Dehazing

Hierarchical Semantic-Guided Contextual Structure-Aware Network for Satellite Image Dehazing

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

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