Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features

Li, Shuping; Yuan, Qianhao; Zhang, Yeming; Lv, Bao-Zhan; Feng, Wei

doi:10.3390/app12178552

Cited by 14 publications

(2 citation statements)

References 16 publications

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“…The encoder module of the network uses hybrid convolution combined with standard convolution and extended convolution to expand the receptive field to extract more detailed image features. Li et al [29] proposed an end-to-end defogging algorithm. First, the shallow features of the haze image were extracted by a preprocessing module.…”

Section: Deep Learning Defogging Algorithmmentioning

confidence: 99%

Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network

et al. 2023

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With the wave of artificial intelligence and deep learning sweeping the world, there are many algorithms based on deep learning for image defog research. However, there is still serious color distortion, contrast reduction, incomplete fog removal, and other problems. To solve these problems, this paper proposes an improved image defogging network based on the traditional cycle-consistent adversarial network. We add the self-attention module and atrous convolution multi-scale feature fusion module on the basis of the traditional CycleGAN network to enhance the feature extraction capability of the network. The perceptual loss function is introduced into the loss function of the model to enhance the texture sense of the generated image. Finally, by comparing several typical defogging algorithms, the superiority of the defogging model proposed in this paper is proved qualitatively and quantitatively. Among them, on the indoor synthetic data set, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) of the network designed by us can reach 23.22 and 0.8809, respectively. On the outdoor synthetic data set, the PSNR and SSIM of our designed network can be as high as 25.72 and 0.8859, respectively. On the real data set, the PSNR and SSIM of our designed network can reach 21.02 and 0.8166, respectively. It is proved that the defogging network in this paper has good practicability and universality.

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Section: Deep Learning Defogging Algorithmmentioning

confidence: 99%

Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network

et al. 2023

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“…In 2022, Li et al [24] suggested a hybrid imagedefogging approach using a Vision Transformer and a convolutional neural network. The pre-processing function first extracted the flat features of the hazy image.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-APT: Deep Learning based Efficient Accident Prevention Technique in Fogged Environment

Lokesh

Ahilan³

2023

Preprint

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Image defogging is an innovative strategy for recovering images in foggy environments that has gotten a lot of attention in recent years because of its use in surveillance systems. The standard defogging algorithm, on the other hand, has difficulty merging the depth of picture detail and the colour of the picture. In this paper, a novel Accident Prevention Technique (Deep-APT) has been proposed to effectively restore fog-free images and prevent accidents using FasterRCNN network. Initially, a dashboard camera monitors the road ahead of the vehicle and collects video. This video sequence is converted to frames. The transformed images are pre-processed using an Adaptive dual threshold Tetrolet transform that preprocess foggy images to fog-free images it is used to remove noise in the input image. Based on the defogged image, use FasterRCNN technology to detect objects in front of the car. The Deep-APT method has been simulated using MATLAB. The experimental result shows the proposed Deep-APT yields an overall accuracy is 99.52%. As compared to existing techniques, the proposed FasterRCNN network shows better results in terms of precision, F1 score, accuracy, and recall. Using DAWN dataset, the MSE, SSIM and PSNR values for the proposed method are 0.12, 0.65 and 0.12. The Deep-APT network improves the overall accuracy of 15.43%, and 4.72% better than CR-YOLnet, and RDL respectively.

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Exploring conditional pixel-independent generation in GAN inversion for image processing

Huang,

Sun,

Tian

et al. 2024

Multimed Tools Appl

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Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features

Cited by 14 publications

References 16 publications

Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network

Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network

Deep-APT: Deep Learning based Efficient Accident Prevention Technique in Fogged Environment

Exploring conditional pixel-independent generation in GAN inversion for image processing

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