Recent advancement in haze removal approaches

Khan, Hira; Xiao, Bin; Li, Weisheng; Muhammad, Nazeer

doi:10.1007/s00530-021-00865-8

Cited by 11 publications

(4 citation statements)

References 133 publications

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“…Presently, the dominant approaches for image dehazing can be classified into two main groups [4][5][6][7]: those founded on physical models and those using deep learning. The former approach mainly focus on the mechanisms underlying the degradation of hazy images.…”

Section: Introductionmentioning

confidence: 99%

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Wang,

Zhao,

Yao

et al. 2024

Remote Sensing

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Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close range or mid-range, while ignoring long-range scenes such as aerial perspective. Furthermore, based on the atmospheric scattering model, the inclusion of depth information is essential for the procedure of image dehazing, especially when dealing with images that exhibit substantial variations in depth. However, most existing models neglect this important information. Consequently, these state-of-the-art (SOTA) methods perform inadequately in dehazing when applied to long-range images. For the purpose of dealing with the above challenges, we propose the construction of a depth-guided dehazing network designed specifically for long-range aerial scenes. Initially, we introduce the depth prediction subnetwork to accurately extract depth information from long-range aerial images, taking into account the substantial variance in haze density. Subsequently, we propose the depth-guided attention module, which integrates a depth map with dehazing features through the attention mechanism, guiding the dehazing process and enabling the effective removal of haze in long-range areas. Furthermore, considering the unique characteristics of long-range aerial scenes, we introduce the UAV-HAZE dataset, specifically designed for training and evaluating dehazing methods in such scenarios. Finally, we conduct extensive experiments to test our method against several SOTA dehazing methods and demonstrate its superiority over others.

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Section: Introductionmentioning

confidence: 99%

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Wang,

Zhao,

Yao

et al. 2024

Remote Sensing

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“…Researchers have attempted to address this issue by using physical models, such as estimating the projection map

t(x)

based on the observed hazy image

I(x)

in order to recover the clear image or scene brightness

J(x)

. However, the inherent challenges of the ill‐posed problem have not yet been fully resolved, making image defogging a persistently difficult problem [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Foggy image restoration using deep sub‐pixel reconstruction network

Li,

Liu,

Shi

et al. 2023

IET Image Processing

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Light undergoes attenuation due to scattering and refraction when propagating through aerosols. In foggy conditions, Aerosol particles in the troposphere exhibit high mobility introducing intricate non‐linear noise into images. Foggy image restoration represents an ill‐posed problem, where traditional physical models and image enhancement techniques often prove inadequate in delivering effective solutions. This paper introduces a novel deep sub‐pixel reconstruction algorithm for foggy image restoration, pioneering the application of sub‐pixel reconstruction modules to this domain. This model employs convolutional layers to extract low‐level features and dense‐connected layers for high‐level feature extraction. Furthermore, a specialized sub‐pixel reconstruction module tailored for the task of foggy image restoration is designed, with the purpose of reconstructing dehazed images from latent vectors. During training, a generative adversarial training framework is adopted, incorporating a purpose‐designed discriminator. Additionally, a fusion loss is implemented to facilitate model refinement. Quantitative and qualitative evaluation experiments conducted on synthetic and real‐world image datasets demonstrate the effectiveness of the proposed method in preserving finer details. The Structural Similarity Index (SSIM) is observed to improve by 2.5%, attesting to enhanced perceptual quality for grayscale foggy images.

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“…The theory also indicates that complex scenes caught by HVS are decomposed into relative illumination and reflectance components [2]. There are various multimedia applications based on the relation: low-light enhancement [5][6][7][8][9][10][11], high-dynamic range (HDR) imaging [12], haze removing [13][14][15], under-water image enhancement [16,17], shadow removing [18], denoising [19], medial imaging [20,21], and face recognition [22].…”

Section: Introductionmentioning

confidence: 99%

Retinex-Based Light Source Generation for Relighting up Night Photography

Oishi

Fukushima

2022

Preprint

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Purpose: The lighting up of buildings is one of the entertainments that make a city more colorful, and photographer sometimes changes the lighting by using photo-editing applications. This paper proposes a method for automatically performing such an application that follows the Retinex theory. The Retinex theory indicates that the complex scenes caught by the human visual system are decomposed into illumination and reflectance components. Many Retinex applications take much cost into the decomposition, while we show that the decomposition accuracy is less dependent on the final output for the nightscape relighting. Methods: First, we propose an extracting method of a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area in the limited area. We show that relighting within the limited area can be represented without illumination and reflectance decomposition. We then show that this fact can be exploited to realize this application using a simple Retinex decomposition of the single-scale Retinex with fast edge-preserving filters to represent various lighting effects. Results: Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced. Also, the proposed various diffusion method can enhance night skyline photographs with various expressions. Conclusion: In this paper, we proposed a relighting method for night skyline images based on the Retinex theory. Unlike typical Retinex applications, the proposed method simplifies the process by carefully extracting the lighting points instead of performing a complex illumination and reflectance decomposition and allows various effects using various diffusion methods.

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Recent advancement in haze removal approaches

Cited by 11 publications

References 133 publications

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Foggy image restoration using deep sub‐pixel reconstruction network

Retinex-Based Light Source Generation for Relighting up Night Photography

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