Single image haze removal using content‐adaptive dark channel and post enhancement

Li, Bo; Wang, Shuhang; Zheng, Jiangbin; Zheng, Liping

doi:10.1049/iet-cvi.2013.0011

Cited by 56 publications

(34 citation statements)

References 38 publications

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“…The relevant comparison results are provided in Figure 13b-e and Figure 13g-j. As we can see from Figure 13, the transmission maps that are estimated using our method are more consistent with our intuition than those of [12,26] and are comparable to those of [21]. …”

Section: Transmission Estimation Comparisonsupporting

confidence: 83%

“…However, to our best knowledge, many transmission estimation methods have been proposed and have made significant progress. In order to verify the advantages of our method, we conduct the experimental comparison with several state-of-the-art methods, including [12,21,26]. We choose two challenging hazy images (a dense haze image without sky region (Figure 13a) and a hazy image with sky region (Figure 13f) for the experimental comparison.…”

Section: Transmission Estimation Comparisonmentioning

confidence: 99%

“…In Figures 14-17 [12,21,26] and our method, respectively; bottom: the corresponding recovered images.…”

Section: Qualitative Comparisonmentioning

confidence: 99%

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An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

Yuan¹,

et al. 2017

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Abstract:In this paper, we propose a single image dehazing method aiming at addressing the inherent limitations of the extensively employed dark channel prior (DCP). More concretely, we introduce the Gaussian mixture model (GMM) to segment the input hazy image into scenes based on the haze density feature map. With the segmentation results, combined with the proposed sky region detection method, we can effectively recognize the sky region where the DCP cannot well handle this. On the basis of sky region detection, we then present an improved global atmospheric light estimation method to increase the estimation accuracy of the atmospheric light. Further, we present a multi-scale fusion-based strategy to obtain the transmission map based on DCP, which can significantly reduce the blocking artifacts of the transmission map. To further rectify the error-prone transmission within the sky region, an adaptive sky region transmission correction method is also presented. Finally, due to the segmentation-blindness of GMM, we adopt the guided total variation (GTV) to tackle this problem while eliminating the extensive texture details contained in the transmission map. Experimental results verify the power of our method and show its superiority over several state-of-the-art methods.

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Section: Transmission Estimation Comparisonsupporting

confidence: 83%

Section: Transmission Estimation Comparisonmentioning

confidence: 99%

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An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

Yuan¹,

et al. 2017

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“…But its performance on the sky region and white objects is unsatisfactory, and the time complexity of soft matting is very high. As the best algorithm at that time, many algorithms improve the DCP method in some particular aspects [2][3][4]. Chen et al [5] divide the image into foreground and background based on Fisher's linear discriminant, to process images with a dramatic depth change.…”

Section: Introductionmentioning

confidence: 99%

Fast Video Dehazing Using Per-Pixel Minimum Adjustment

Luan

Zeng

Shang

et al. 2018

Mathematical Problems in Engineering

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.To reduce the computational complexity and maintain the effect of video dehazing, a fast and accurate video dehazing method is presented. The preliminary transmission map is estimated by the minimum channel of each pixel. An adjustment parameter is designed to fix the transmission map to reduce color distortion in the sky area. We propose a new quad-tree method to estimate the atmospheric light. In video dehazing stage, we keep the atmospheric light unchanged in the same scene by a simple but efficient parameter, which describes the similarity of the interframe image content. By using this method, unexpected flickers are effectively eliminated. Experiments results show that the proposed algorithm greatly improved the efficiency of video dehazing and avoided halos and block effect.

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“…Although Li [18] adopted postenhancement processing to improve the visual quality, he was unable to analyze the underlying key problem and, consequently, failed to make an essential improvement on dehazing. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement.…”

Section: Introductionmentioning

confidence: 99%

Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.

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Single image haze removal using content‐adaptive dark channel and post enhancement

Cited by 56 publications

References 38 publications

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

Fast Video Dehazing Using Per-Pixel Minimum Adjustment

Single image dehazing via an improved atmospheric scattering model

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