Multi-Feature-Based Bilinear CNN for Single Image Dehazing

Fu, Hui; Wu, Bin; Shao, Yanhua

doi:10.1109/access.2019.2920537

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…However, this method is not practical as it cannot properly remove haze from the non homogeneous region and unnecessary textures in the scene depth exists. Presently, Artificial Intelligence (AI) based methods are also being used for defogging images [22][23][24][25][26][27] and deliver some promising results. However, these methods have higher computational complexity than traditional statistical based defogging approaches.…”

Section: Restoration-based Defogging Approachesmentioning

confidence: 99%

Image Defogging Framework Using Segmentation and the Dark Channel Prior

Anan

Khan

Kowsar

et al. 2021

Entropy

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Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

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Section: Restoration-based Defogging Approachesmentioning

confidence: 99%

Image Defogging Framework Using Segmentation and the Dark Channel Prior

Anan

Khan

Kowsar

et al. 2021

Entropy

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“…Defogging was considered an image-to-image conversion task and an enhanced Pix17pix network was proposed to handle it. Fu [3] et al employed a multi-feature-based bi-linear CNN to reduce the halo effect around abrupt edges and to suppress image noise. The densely connected pyramidal defogging network was proposed by Zhang and Patel to check both scene depth and atmospheric light.…”

Section: Introductionmentioning

confidence: 99%

Research on smoke removal of industrial scene image based on an improved dark channel prior

Liang

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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In response to the challenge of obtaining clear monitoring images in non-ferrous metal smelters, particularly under complex atmospheric conditions and varying levels of dust and fog concentrations, we have designed an indoor dust and fog image clarification algorithm in this paper. This algorithm utilizes a concentration evaluation to obtain fog weights and construct a refined transmittance in dense fog regions. Transmittance values for non-dense fog regions are calculated using the a priori dark channel and ambient light matrix of the region. The Alpha blending technique is used to fuse ambient light and transmittance matrices of different regions, while bootstrap filtering is utilized to suppress generated noise and preserve image edge information. Finally, the global ambient light and transmittance values are obtained and substituted into the atmospheric scattering model to recover low-illumination dust and fog images. Experimental results demonstrate that the proposed algorithm achieves excellent performance in non-ferrous metal smelting environments by effectively reducing fog concentration in images, improving image illumination, and preserving image edge information.

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“…Dehazing was regarded as an image-to-image translation task, and an enhanced Pix2pix network was proposed to handle it in [17]. Fu et al [18] employed the multi-feature-based bilinear CNN in order to reduce the halo effect around the abrupt edges and restrain image noise. The densely connected pyramid dehaze network was proposed by Zhang and Patel [19], which can simultaneously examine scene depth and atmospheric light.…”

Section: Introductionmentioning

confidence: 99%

Robust Single-Image Dehazing

Kim

2021

Electronics

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This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmissions of bright regions or objects that can generate color distortions during the process of dehazing. In order to suppress the distortions in a large sky area or a bright white object, the sky probabilities and the white-object probabilities calculated in the non-sky area are proposed. The sky area is detected by combining the advantages of a region-based and a boundary-based sky segmentation in order to consider various sky shapes in road scenes. The performance of the proposed methods is evaluated using synthetic and real-world datasets. When compared to conventional methods in the reviewed literature, the proposed method produces significant improvements concerning visual and numerical criteria.

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Multi-Feature-Based Bilinear CNN for Single Image Dehazing

Cited by 14 publications

References 38 publications

Image Defogging Framework Using Segmentation and the Dark Channel Prior

Image Defogging Framework Using Segmentation and the Dark Channel Prior

Research on smoke removal of industrial scene image based on an improved dark channel prior

Robust Single-Image Dehazing

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