A physics based generative adversarial network for single image defogging

Liu, Wei; Yao, Rongguo; Qiu, Guoping

doi:10.1016/j.imavis.2019.10.001

Cited by 22 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, each suspended particle in the gas can be regarded as a separate scatterer. Generally speaking, the scattering intensity does not affect each other [ 15 , 16 ]. According to the study of atmospheric scattering mechanism, Mie scattering mechanism can be used to analyze the scattering effect of dust, mist, fog, dense fog, and other adverse weather conditions [ 17 , 18 ].…”

Section: A Priori Methods Of Dark Channelmentioning

confidence: 99%

“…It can be seen from equation (15), in the region with high-fog concentration, W ′ (x) has a larger value, which makes J c min (x) have a stronger pixel value in the dense fog region. In the close range area, if W ′ (x)W ′ (x) is small, the intensity of J c min (x) is low, even close to 0. is is in line with the theory that dark channels tend to zero proposed by He and Sun [30].…”

Section: Transmittance Estimation Based On Fog Concentrationmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

show abstract

Section: A Priori Methods Of Dark Channelmentioning

confidence: 99%

Section: Transmittance Estimation Based On Fog Concentrationmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…Zeng et al [144] proposed an illumination-adaptive network for person detection, which is able to eliminate the impact of illumination discrepancy. Besides using operators and filters, some generative-based approaches are applied [145][146][147][148] . Although these works enhance the performance of CNN models on foggy images, there is still room for improvement.…”

Section: Environmental Impact On Agriculture Datamentioning

confidence: 99%

Deep learning approaches for object recognition in plant diseases: a review

Zhou,

Zhang,

et al. 2023

Intelligence & Robotics

View full text Add to dashboard Cite

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

show abstract

“…Domain adaptation for foggy scenes segmentation Dehazing methods and synthetic fog techniques have been the two primary domain adaptation methods for segmenting foggy scenes. Dehazing techniques aim to improve the color quality or contrast of images by reducing haze [37][38][39][40][41][42]. However, the performance of dehazing methods may suffer as the fog density increases, and severe dehazing may lead to image degradation.…”

Section: Pertinent Workmentioning

confidence: 99%

MIFormer: An Effective Semantic SegmentationNetwork for Foggy Weather conditions

Ge,

Huo,

et al. 2023

Preprint

View full text Add to dashboard Cite

Reliable operation is a key metric for autonomous driving, but the factors related to the environment such as weather and lighting can impair the perception capability of autonomous vehicles. Unsupervised adaptation in foggy circumstances is difficult due to the significant fluctuations in the scene's visibility caused by weather conditions, including smog, fog, and haze. This paper presents an effective network using multi-teacher intermediate domain adaptation (MIFormer), which offers a fresh approach to adapting semantic segmentation for the purpose of handling dense foggy scenes. This network integrates training from an intermediate domain to reduce the domain gap caused by heavy fog weather. To further increase segmentation accuracy, we propose three training strategies: Rare Class Sampling, Multi-teacher knowledge distillation, and Mixup improved by Uncertainty principle. We assess the performance of the suggested model across scenarios involving the adaptation from actual clear-weather scenes to real foggy scenes, as well as from synthetic non-foggy images to real foggy scenes. The results have demonstrated that MIFormer’s performance in semantic segmentation with foggy images is better than that with ResNet-38, BDL, CBST, MLST and FogAdapt. In particular, when adhering to the standard configurations and comparing against some mainstream methods, MIFormer achieves a segmentation performance of 69.1 mIoU adapted from GTA to foggy-Cityscapes and 61.8 mIoU adapted from Synthia to foggy-Cityscapes. Additionally, it can even segment rare classes effectively, such as train, bus, and truck.

show abstract

A physics based generative adversarial network for single image defogging

Cited by 22 publications

References 19 publications

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Deep learning approaches for object recognition in plant diseases: a review

MIFormer: An Effective Semantic SegmentationNetwork for Foggy Weather conditions

Contact Info

Product

Resources

About