Visibility dehazing based on channel-weighted analysis and illumination tuning

Lee, Jung-San; Li, Chih-Hsuan; Lee, Hsinyu

doi:10.1007/s11042-018-6280-2

Cited by 4 publications

(18 citation statements)

References 11 publications

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“…Regrettably, the scheme of Huang et al may bring in unnatural recovered image due to discordance procedure between dehazing and de-sandstorming. To overcome the weak point of [11], Lee et al have introduced channel-weighted analysis and employed the illumination adjustment after recovering procedure [16]. This can acquire favorable human visual perception of a photo under wild weather conditions.…”

Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

“…The background might lose the real appearance in an image. The outputs of above methods are overexposure in the regions of sky and thick fog because they have employed the same intensity of transmission map [7][8][9][10][11][12][13][14][15][16][17]. It is not accurate enough for specific parts, including foreground and background of an image.…”

Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

“…This can get rids of the subject of overadjustment. Furthermore, the proposed scheme has introduced the channel-weighted analysis [16] to refine the details and balance the irregular lightness. The experimental results have demonstrated that the new method can outperform the latest state-of-the-art methods [9-12, 15-17, 25] in terms of subjective and the objective assessments, including human perception, peak signal to noise ratio (PSNR), and structural similarity index (SSIM).…”

Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

“…The dehazing method could be classified into three categories: additional information approach [1][2][3], multiple-image approach [4][5][6], and single-image approach [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. In the terms of additional information approach, a dehaze model is created from the depth of fields (DOF).…”

Section: Introductionmentioning

confidence: 99%

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Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

Lee¹,

Lee²,

Fan³

et al. 2022

Journal of Internet Technology

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<p>Computer vision techniques are widely applied to the object detection, license plate recognition, remote sensing, and outdoor monitoring system. The performance of these applications mainly relies on the high quality of outdoor image. However, an outdoor image can be led to contrast decrease, color distortion, and unclear structure by poor weather conditions and human factors such as haze, fog, and air pollution. These issues may lower down the sharpness of a photo. Despite of the single-image dehazing is used to solve these issues, it cannot achieve a satisfactory result when the method deals with the bright scene and sky area. In this article, we aim to design an adaptive dehazing technique based on fusion transmission and sky weight detection. The sky weight detection is employed to distinguish the foreground and background, while detected results are applied to the fusion strategy to calculate deep and shallow transmissions. Thus, this can get rids of the subject of over-adjustment. Experimental results have demonstrated that the new method can outperform the latest state-of-the-art methods in terms of subjective and the objective assessments.</p> <p> </p>

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Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

Section: Figure 1 Optical Scattering Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

Lee¹,

Lee²,

Fan³

et al. 2022

Journal of Internet Technology

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“…However, over-enhancement may still occur. And a newly proposed channel-weighted analysis [23] aims to eliminate the unnatural effect of DCP method. Another two approaches based on hue disparity prior and color attenuation prior are proposed by Ancuti et al [24] and Zhu et al [25], respectively.…”

Section: Introductionmentioning

confidence: 99%

Single Image Haze Removal Based on a Simple Additive Model With Haze Smoothness Prior

Zhang

Wang

Tang

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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Single image haze removal, which is to recover the clear version of a hazy image, is a challenging task in computer vision. In this paper, an additive haze model is proposed to approximate the hazy image formation process. In contrast with the traditional optical model, it regards the haze as an additive layer to a clean image. The model thus avoids estimating the medium transmission rate and the global atmospherical light. In addition, based on a critical observation that haze changes gradually and smoothly across the image, a haze smoothness prior is proposed to constrain this model. This prior assumes that the haze layer is much smoother than the clear image. Benefiting from this prior, we can directly separate the clean image from a single hazy image. Experimental results and comparisons with synthetic images and real-world images demonstrate that the proposed method outperforms state-of-the-art single image haze removal algorithms.

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An airlight estimation method for image dehazing based on gray projection

Wang

Yuan

et al. 2020

Multimed Tools Appl

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Visibility dehazing based on channel-weighted analysis and illumination tuning

Cited by 4 publications

References 11 publications

Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

Single Image Haze Removal Based on a Simple Additive Model With Haze Smoothness Prior

An airlight estimation method for image dehazing based on gray projection

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