A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods

Agrawal, Subhash Chand; Jalal, Anand Singh

doi:10.1007/s11831-022-09755-2

Cited by 33 publications

(15 citation statements)

References 170 publications

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“…Traditional dehazing methods have shown good results on synthetic haze but real-world dehazing can result in color distortion and incomplete removal of haze. 17 In this paper, the synthetic haze dataset and OTS dataset are used for training the dehazing network, and the test results on realworld haze are compared in Fig. 4.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Enhancing haze removal and super-resolution in real-world images: a cycle generative adversarial network-based approach for synthesizing paired hazy and clear images

Zhuge²,

Wang

et al. 2023

Opt. Eng.

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.Haze significantly impacts various fields, such as autonomous driving, smart cities, and security monitoring. Deep learning has been proven effective in removing haze from images. However, obtaining pixel-aligned hazy and clear paired images in the real world can be challenging. Therefore, synthesized hazed images are often used for training deep networks. These images are typically generated based on parameters such as depth information and atmospheric scattering coefficient. However, this approach may cause the loss of important haze details, leading to color distortion or incomplete dehazed images. To address this problem, this paper proposes a method for synthesizing hazed images using a cycle generative adversarial network (CycleGAN). The CycleGAN is trained with unpaired hazy and clear images to learn the features of the hazy images. Then, the real haze features are added to clear images using the trained CycleGAN, resulting in well-pixel-aligned synthesized hazy and clear paired images that can be used for dehaze training. The results demonstrate that the dataset synthesized using this method efficiently solves the problem associated with traditional synthesized datasets. Furthermore, the dehazed images are restored using a super-resolution algorithm, enabling the obtainment of high-resolution clear images. This method has broadened the applications of deep learning in haze removal, particularly highlighting its potential in the fields of autonomous driving and smart cities.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Enhancing haze removal and super-resolution in real-world images: a cycle generative adversarial network-based approach for synthesizing paired hazy and clear images

Zhuge²,

Wang

et al. 2023

Opt. Eng.

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“…Hazy images adversely affect crucial processes involving image recognition like ship detection, road safety, landing flights in fog, remote sensing and many more such applications 1 . This makes image dehazing an important pre-processing technique to ensure that the image processing algorithms don't go haywire on hazy images for computer vision tasks like object detection, classification and so on 2 . Hazy images lose a lot of their visibility and contrast that makes accurate depth extraction from these images a daunting task.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks

Kaushik

Gupta

Nehete

et al. 2023

AI and Optical Data Sciences IV

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Optical imaging sensors suffer from distortions caused by atmospheric particles such as dust, mist, fog, haze, and smoke, resulting in degradation of object detection and recognition. To circumvent these issues, image dehazing is an essential preprocessing stage for various real time applications. Several conventional dehazing methods rely on the haze formation model that are inherently dependent on a large number of variables, requiring huge computational burden on the processor. This severely affects the dehazing performance and also restricts real time processing. To overcome these issues, this work deals with an end-to-end real time dehazing architecture based on light weight Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). Proposed depthwise seperable and residual (DSR) block has been used instead of convolution layers that significantly lowered the parameters and computations. Furthermore, sigmoid and bilateral ReLu activation functions have been exploited to prevent oversaturation of dehazed images. The proposed model achieves significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for both synthetic and real-world hazy images, when compared to other architectures such as dark channel prior (DCP) and DehazeNet. The performance outcome of CNN and GAN based dehazing architectures are analyzed and compared.

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“…LiDAR was used in [ 25 ] to generate a grayscale depth image, and then scattering coefficients were found and used to estimate transmittance; finally, a dehazed image was obtained by using the hazy image model. For a recent comprehensive review on image dehazing, see [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm

Hsieh

Chen

Chang

2023

Sensors

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Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.

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A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods

Cited by 33 publications

References 170 publications

Enhancing haze removal and super-resolution in real-world images: a cycle generative adversarial network-based approach for synthesizing paired hazy and clear images

Enhancing haze removal and super-resolution in real-world images: a cycle generative adversarial network-based approach for synthesizing paired hazy and clear images

Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks

Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm

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